Photo of Associate Professor Md Zahidul Islam Associate Professor Zahid Islam

PhD University of Newcastle Australia, Grad Dip UNSW Australia, BSC in Engineering RUET Bangladesh

Zahid Islam (Full name: Md Zahidul Islam) is an Associate Professor in Computer Science, in the School of Computing and Mathematics, which is a top Computer Science school in Australia (See Here).

He is a Deputy Director of the Data Science Research Unit (DSRU), Leader of the Data Mining Research Area, Deputy Leader of the Health Services Research Area and Course Co-ordinator of Bachelor of Computing (Honours) of the Faculty of Business Justice and Behavioural Sciences, Charles Sturt University, Australia.

His main research interests are in Data Mining, Classification and Clustering algorithms, Missing value analysis, Outliers detection, Data Cleaning and Preprocessing, Privacy Preserving Data Mining, Privacy Issues due to Data Mining on Social Network Users, and Applications of Data Mining in Real Life.

For a brief overview on his research interest please download the One Page Research Overview OR visit the Research link. You may also want to download two-slide power point presentation OR visit the Research link.

You may also want to watch short videos on his data mining techniques at the Youtube channel called Zahid's Data Mining Channel.

You may also want to visit his Facebook page called Data Mining Research Group for general discussion on data mining.

Zahid has received a number of awards which can be accessed at the Latest News link.

He is a co-recipient of a number of industry grants which can be accessed at the Grants link.

He has published many peer reviewed journal articles and conference papers in high impact journals and conferences. A complete list of his publications can be accessed at the Publications link where a pre-print for almost all them are available.

He has successfully supervised (as the principal supervisor) a number of PhD students Honours students. A complete list of his students can be seen at Research link. Any potential PhD students are encouraged to contact him via email at zislam at csu.edu.au

He serves as a Conference Co-Chair, Program Committee Co-Chair, Session Chair, Section Editor and Reviewer of journals and PhD theses, as presented at the Grants link.

He is also the Course Coordinator of the Honours course at the School of Computing and Mathematics. Feel free to watch a brief Webinar presentation by him on the Honours course in You Tube at https://youtu.be/fikCl0RpN6I.

He has teaching experiences at the University of Newcastle and Charles Sturt University, Australia.

A Relaxed afternoon with the Data Mining Research Group at CSU after my Seminar in April, 2015 The Data Mining Research Group - A Happy and Active Research Team.

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My Teaching Philosphy   |    Teaching Responsibilities   |    CELT Evaluation   |    Student Evaluation

My Teaching Philosphy

I am exploring various teaching pedagogy to find the one that suits the best for me. I change my teaching approach from subject to subject. Sometimes, I concentrate to give students as much information as possinble. However, in some other situations I prefer to allow my students to explore, share and learn by themselves.

For example, in a subject like data mining, graph theory and networking students need to know the theory in the background. In this type of subjects I take a research type approach for my students where my role as a lecturer is to provide them the initial knowledge about a topic. I then give them an opportunity to figure out the advantages and disadvantages of the topic, and a possible solution of the problem. The possible solution leads them towards the next topic.

After the initial introduction my role is to guide them towards the right direction. In a lecture room I allow them to work in groups for sometime so that every group can come up with a possible solution.

For example, I provide the initial information and a general introduction if I am teaching encryption techniques in Cryptography or Network Security subject. I then encourage my students to figure out the advantages and disadvantages of encryption techniques such as Playfair Cipher and Transposition Cipher. Once they figure out the drawbacks of these techniques I engage them to work out a possible solution. Once they reach a level then I provide the next level of information so that they can proceed further. We continue this in a very friendly class room environment until they reach the state of the art. I found this teaching technique as very effective to make students interested and engaged in a topic. I think this way they also learn something that they remember for long time. They get a feeling that as if they invented the state of the art technique.

However, in a practical type subject (such as web development and programing language) I encourage them to learn and code by themselves to enjoy their work. I prefer to show them example of what can be done and ask them to do similar things.

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Teaching Responsibilities

Session 1, 2012
  • ITC106 Programming Principles Bathurst Internal
  • ITC106 Programming Principles Bathurst Distance
  • ITC303 Software Engineering Project 1 Bathurst Internal
  • ITC303 Software Engineering Project 1 Bathurst Distance
Semester 2, 2011
  • ITC242 Introduction to Data Communications
  • ITC431 Computer Networks
Semester 1, 2011
  • ITC161 Introduction to Information Technology
  • ITC331 Ethics and Professional Practice
  • ITC514 Network and Security Administration using Linux
  • ITC555 Linux Networking and Security
Semester 2, 2010
  • ITC114 Database Management Systems
  • ITC242 Introduction to Data Communications
  • ITC431 Computer Networks
Semester 1, 2010
  • ITC161 Introduction to Information Technology
  • ITC331 Ethics and Professional Practice
  • ITC514 Network and Security Administration using Linux
  • ITC555 Linux Networking and Security
Semester 1, 2009
  • ITC570 Special Subject in IT - Data Mining
  • ITC105 Communication and Information Management
  • ITC532 IT Specialisation Project
  • ITC233 Network Engineering 1
  • ITC493 IT Project Management
Trimester 2, 2009
  • ITC514 Network and Security Administration using Linux
Semester 2, 2009
  • ITC 242 Introduction to Data Communications
  • ITC 431 Computer Networks
  • ITC 499 IT Project Proposal
Other Teaching Experiences
  • ITC 518 Principles of Programming using C#
  • SENG 4420 Software Architecture
  • COMP 1050/6050 Internet Communications
  • SENG 3100 Advanced Software Process
  • Administrative Responsibilities
  • Associate Course coordinator BIT
  • Associate Course coordinator BIT/BBus

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CELT Evaluation

Please feel free to have a look at Centre for Enhancing Learning and Teaching (CELT), Charles Sturt University Evaluation on my teaching.

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Student Evaluation

Please feel free to have a look at the evaluations made by my beloved students at Charles Sturt University and Newcastle University on my teaching style.I have to say that my students are very generous to me and they seriously love me. They are all wonderful students. .(This is still under construction as I have not uploaded all of them yet.)

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PhD Students   |    My Research Focus   |    My Research Interest   |   

Research

Supervisor of the following PhD Students
PhD Students Status Thesis Title My Role
Dr Md Anisur Rahman, PhD Completed Automatic Selection of High Quality Initial Seeds for Generating High Quality Clusters without Requiring any User Inputs. Principal Supervisor
Dr Md Geaur Rahman, PhD Completed Data Cleansing for Data Quality Improvement in Data Mining. Principal Supervisor
Dr Samuel Fletcher Completed Data Mining and Privacy: Modeling Sensitive Data with Differential Privacy Principal Supervisor
Dr Md Nasim Adnan Completed Decision Tree and Decision Forest Algorithms: On Improving Accuracy, Efficiency and Knowledge Discovery. Principal Supervisor
Dr Abul Hashem Beg Completed A Novel Genetic Algorithm based Clustering and Tree based validation in Producing and Evaluating Sensible Clusters Principal Supervisor
Michael Siers Current PhD student Software Defect Prediction by Novel Data Mining Algorithms Addressing Class Imbalance and Cost Sensitivity Issues. Principal Supervisor
Mohammad Khubeb Siddiqui Current PhD student Brain Data Mining for Disease Detection and Prediction. Principal Supervisor
Khondker Jahid Reza Current PhD student Novel Techniques to Protect Privacy of Online Social Media Users from Malicious Data Miners. Principal Supervisor
Darren Bradley Yates Current PhD student 3D Data Mining and Visualisation. Principal Supervisor
Nectarios Costadopoulos Current PhD student Data Mining for Emotion Detection through Wearable Devices. Principal Supervisor
Dr Mahmood Khan Completed A Web Based Decision Support System Using Geoinformatics Techniques for Irrigation Water Management in a Near Real Time Environment. Co-supervisor
Dr Rath Kanha Sar Completed The Tracking of Users' Unintentionally Shared Information by Social Network Sites. Co-supervisor

Besides, I have co-supervised two (2) other PhD students. Both of them have successfully completed their PhD studies.
Principal Supervisor of the following Honours Students
  1. Darren Yates - Honours. (Completed.)
  2. Michael Siers - Honours. (Completed.)
  3. Michael Furner - Honours. (Completed.)
  4. Peter Hough - Honours. (Completed.)
  5. Samuel Fletcher - Honours. (Completed.)
Five honours students under my principal supervision have achieved Class 1 Honours which is the best possible result in honours.

My PhD and Honours students (under my principal supervision) have received the following awards:
  • University Medal in 2014
  • Faculty of Busiess Executive Deans List Award in 2014 and in 2016 (two awards in 2016).
  • School of Computing and Mathematics Honours Academic Excellence Award in 2014 and in 2016.
  • Best paper award in AusDM 2013 (ERA Rank B)
  • Best presentation award in the SCM RHD Symposium 2014
  • Best postar award in the SCM RHD Symposium 2016
Potential PhD students are welcome to contact him via email at zislam@csu.edu.au to discuss possibilities of any scholarships.

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My Research Focus

Our main research interests include data mining algorithms and their applications. For example, if you have a dataset with some missing and incorrect values we can clean that up for you by imputing the missing values, identifying (and making corrections of) the corrupt values, and carrying out various other pre-processing tasks. The datasets cleaned up by our algorithms are more useful and accurate for various statistical and data mining analyses. Moreover, the algorithms automatically learn the properties of a dataset, identify (and make necessary corrections of) incorrect values, and impute/estimate missing values without requiring any user input and domain knowledge. Of course with user input and domain knowledge we can carry out additional cleaning, but that is not a requirement.

We also analyse data and extract patterns from them through our classification algorithms that build decision trees and decision forests from datasets. The extracted patterns will help you to understand your datasets better. While understanding your data through conventional statistical analyses such as correlation calculation you need to assume the presence or absence of a relationship between two attributes/features such as Productive Employees and Salary, our pattern extraction (knowledge discovery) algorithms do not require any assumptions. Instead they automatically find such relationships and logic rules. You simply need to give them your questions such as Why some employees are productive and some are not? and the algorithms will come up with all possible answers and their statistical significances. With the discovered knowledge our algorithms can then also predict the future; whether or not a potential new employee will be productive. That sounds interesting!

We are also interested in clustering that can find useful groups of records (such as customers and patients) having similar properties. Our in house clustering algorithms do not require any user input whatsoever including the number of clusters. They also allow you to put different weights (if you wanted) varying from 0 to 1 on the attributes to indicate the significance levels of the attributes for your clustering purpose.

Privacy Preserving Data Mining is our other research focus. If you want to release your dataset for public use, but are concerned about the privacy of the data subjects our privacy preserving techniques will allow you to add noise to the datasets for preserving the privacy while maintaining the quality of the data. Another recent research focus is the possible threats from data mining on the privacy of online social network site users and their technical solutions.

Each dataset comes with its own challenges and requirements. For example, some datasets are very unstable in nature having high dimension (few thousand of attributes) and low size (only few records). Some datasets are very imbalanced in the sense that they have huge number of records (say 99.99% of the total records) of one class/group such as Non-Cancer and only few records (say 0.01%) of the other class such as Cancer. The datasets can also be time series, sequential and tabular having various types of attributes including categorical, numerical, binary, nominal and ordinal. Since we develop in house and custom-made algorithms, we can cater for your domain specific requirements and challenges. We are also interested to help you to design your survey questions in order to build a useful dataset.

We have applied our algorithms in irrigation water demand prediction, software defect prediction and employee management. For all these real world problems ­our algorithms were found very useful. Will they also be useful in analysing yours?

Two Slide Presentation.

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My Research Interest

  • Data Mining,
  • Classification Algorithm,
  • Clustering Algorithm,
  • Missing Value Imputation,
  • Outliers Detection,
  • Data Cleansing and Preprocessing,
  • Decision Forest Algorithm,
  • Decision Tree Algorithm,
  • Intelligent Decision Support System,
  • Application of Data Mining such as Water Demand Prediction and Property Valuation,
  • Privacy Preserving Data Mining,
  • Privacy Preserving Multi-party Secure Data Mining,
  • Data Mining threats on Privacy of Online Community,
  • Trust Model,
  • Security of Sensor Networks,
  • Impact of IT on Society (such as Digital Divide),
  • Hydroinformatics,
  • Graph Labelling,
  • and so on.

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External Grants:

  1. External Funding from Murrumbidgee Local Health District (MLHD), NSW Health, Australia. Period 2016 - 2016. Project Title: Patient Risk Stratification Project, CIs: Zahid Islam and Mark Morrison - $52,677 PLUS an additional travel fund of $4,126.75. CSU Research Office Project Reference number: 0000101620.
    This project received an Innovation Award from the Agency for Clinical Innovation (ACI).
  2. External Funding from the Department of Social Services, Australia. Period 2015 - 2017. Project Title: Social and community links - a driver of healthy and active ageing, CIs: Oliver Burmeister (Lead CI), Mark Morrison, Zahid Islam, Maree Bernoth, Rylee Dionigi, and Rahena Akhter - $655,000 ($60,000 to CSU). Partner organisation: Carewest Pty Ltd, Orange, NSW. CSU Research Office Project Reference number: 0000101130.
  3. External Funding from Young and Well Cooperative Research Centre, Australia 2014. Project Title: Synergy ecosystem data storage: medico-legal and ethical challenges, CIs: Dr Oliver Burmeister, Dr Zahid Islam, Dr Maree Bernoth and Ms Carli Kulmar - $16,500. CSU Research Office Project Reference number: 0000101141.
  4. External Funding from Hobart Nursing District, Australia 2013. Project Title: Review of Support Worker Integration with Functional Decline, CIs: Prof Mark Morrison, Dr Maree Bernoth, Dr Oliver Burmeister, and Dr Zahid Islam. - $39,400. CSU Research Office Project Reference number: 0000100780.
  5. External Funding from Carewest, Australia 2013. Project Title: Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community. CIs: Prof Mark Morrison, Dr Oliver Burmeister, Dr Zahid Islam, Dr Ramudu Bhanugopan and Dr Maree Bernoth - $25,000. CSU Research Office Project Reference number: 0000100558

Some Internal Grants:

  1. COMPACT Funding 2014, Project Tile: Development and Applicaiton of Domain Specific Data Mining Techniques to Predict and Explore the Brand Switching Tendency of Mobile Phone Users, CIs: Dr Zahid Islam and Prof Steven D'Alessandro - $11,236.26.
  2. COMPACT Funding 2014, Project Tile: Industry Funding, CIs: Dr Zahid Islam and Prof Junbin Gao - $13,108.80.
  3. Faculty of Business Research Fellowship 2014 - $40,000
  4. Faculty of Business Research Supervision Excellence Award 2014, Charles Sturt University.
  5. Research Excellence Award 2013, School of Computing and Mathematics, Charles Sturt University. Jointly awarded to A/Prof Yeslam Al-Saggaf, Dr Xiaodi Huang, and Dr Zahid Islam - $1,500
  6. CSU Research Infrastructure Block Grants 2013, Title: Image depth estimation, visualization, and quality assessment using intelligent computing with Dr Manoranjan Paul; Professor Junbin Gao; Dr Michael Antolovich; Dr Zahid Islam and Dr Jim Tulip - $50,000
  7. Faculty of Business Research Fellowship 2013 - $40,000
  8. COMPACT Funding for a novel clustering technique, with Prof Bossomaier, Prof Estivill-Castro, and A/Prof Brankovic 2012 - $9124.
  9. Research Center Fellowship 2012 from Center for Research in Complex Systems (CRiCS) - $40,000
  10. Charles Sturt University Research Infrastructure Block Grants (RIBG), 2012 for "Abnormal event detection using eye tracker technology," with Dr Manoranjan Paul, Prof Junbin Gao, Dr Michael Antolovich, and Prof Terry Bossomaier - $43,000.
  11. COMPACT Fund 2011 for a research on Data Cleansing and Data Pre-processing, with Prof Bossomaier and Prof Gao - $18,249.
  12. COMPACT Fund 2011 for research on Data Mining threats on Privacy of Social Network Site (SNS) users, with Dr Al-Saggaf - $7,000.
  13. AusAID Funding of $750,000 on "Improving Water Use Efficiency in Large Irrigation System in Yellow River Basin, China" with A/Prof Mohsin Hafeez, Dr Yann Chemin, and A/Prof John Louis.
  14. CRiCS Special Grant for an Intelligent Decision Support System research - $10,000.
  15. CRiCS Grant for ARC Linkage Grant Preparation - $4,000.
  16. Research Center Fellowship at IC Water 2009 - $40,000.
  17. Faculty Seed Grant from the Faculty of Business, CSU for conducting a research on a novel clustering technique, 2009 - $3,000.
  18. CSU Small Grant for conducting a research on a novel decision tree classification algorithm, 2009 - $6,000.
  19. Faculty of Engineering and Built Environment Postgraduate Research Prize in the Discipline of Computer Science and Software Engineering, University of Newcastle, Australia,2005.

Chairs, Editors & Reviewers:

  1. PhD Thesis Review
    1. Reviewer of a PhD thesis from the Fedaration University, Australia in 2017.
    2. Reviewer of a PhD thesis from the University of Newcastle, Australia in 2017.
    3. Reviewer of a PhD thesis from the Queensland University of Technology in 2017.
    4. Reviewer of a PhD thesis from the University of Technology Sydney in 2016.
    5. Reviewer of a PhD thesis from the University of Tasmania in 2016.
    6. Reviewer of a PhD thesis from the University of New England in 2014.
  2. Chairs and Editors
    1. Section Editor in 2017, Australasian Journal of Information Systems (AJIS)
    2. Conference Co-chair of the 16th Australasian Data Mining Conference, (AusDM 2018), Bathurst, Australia. 28 - 30 November, 2018.
    3. Program Committee (PC) Co-chair of the Australasian Data Mining Conference, (AusDM 2016), Canberra, Australia. http://ausdm16.ausdm.org/
    4. Program Committee (PC) Co-chair of the Australasian Data Mining Conference, (AusDM 2015), Sydney, Australia. http://ausdm15.ausdm.org/home
    5. Session Chair in the 15th Australasian Data Mining Conference (AusDM) 2017, Melbourne, Australia, 19-20 August, 2017.
    6. Session Chair in the the IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24 - 29, 2016.
    7. Session Chair in the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016, Auckland, New Zealand, 19-22 April 2016.
    8. Session Chair in the 9th International Conference on Advanced Data Mining and Applications Hangzhou, China, 14-16 December 2013.
  3. Invited Talks
    1. New England University, Australia, 2004
    2. New England University, Australia, 2013
    3. Deakin University, Australia, 2014
    4. Independent University, Bangladesh, 2014
    5. Griffith University, Australia, 2016
    6. Queensland Institute of Medical Resaerch (QIMR), 2015
    7. Queensland Institute of Medical Resaerch (QIMR), 2016
  4. Reviewer of Journals
    1. Reviewer of Knowledge-Based Systems.
    2. Reviewer of Expert Systems with Applicaitons.
    3. Reviewer of Australasian Journal of Information Systems.
    4. Reviewer of Reviewer of Journal of King Saud University - Computer and Information Sciences.
    5. Reviewer of Journal of Computers.
    6. Reviewer of Information Sciences.
    7. Reviewer of Pattern Recognition.
    8. Reviewer of the Journal of Research and Practice in Information Technology.
  5. PC Members and Reviewers in Conferences
    1. Reviewer of the 1 st International Workshop on Quality of Security (QoSec) in Wireless Sensor Networks, held in conjunction with the 3rd International Conference on Network & System Security (NSS 2009), October 19-21, 2009, Gold Coast, Australia.
    2. Reviewer and PC Member of Eighth ACS/IEEE International Conference on Computer Systems and Applications (AICCSA’ 2010) held in Hammamet, Tunisia in May 2010.
    3. Reviewer of International Conference on Advances in Electrical Engineering (ICAEE), 2011
    4. Member of International Advisory Committee for IEEE-sponsored International Conference on Advances in Electrical Engineering (ICAEE), 2011
    5. Member International Program Committee of the 10th International Conference on E-business (iNCEB2011), will be hold at Asia Hotel, Bangkok, Thailand.
    6. PC Member of International Conference on Computer Science and Information Technology, Indonesia. (CSIT-2013).
    7. PC Member of Australasian Data Mining Conference, (AusDM 2013), Canberra, Australia.
    8. Member of the International Program Committee (IPC) of the 2013 International Conference on Advances in Electrical Engineering, (ICAEE 2013)
    9. PC Member of 3rd International Workshop on Applications and Technologies in Information Security (ATIS 2013), Sydney, Australia.
    10. PC Member of 2nd International Conference on Electronic Design (ICED 2014) in August 2014, Penang Island, Malaysia.
    11. Program Committee Member of the Applications and Technologies in Cyber Security (ATCS) 2014.
    12. Program Committee Member of the ACML Workshop on Learning on Big Data (WLBD) 2016.
    13. Program Committee Member of the 21st Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2017 (Rank A).
    14. Program Committee Member of the 22nd Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018 (Rank A).

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Selected Publications

Please feel free to contact me if you are interested to have a look at any of these published papers.

Preprints of most of my publications are AVAILABLE at my Research Gate profile .

PhD Thesis:

  • Islam, M. Z. (2008): Privacy Preservation in Data Mining through Noise Addition, PhD thesis in Computer Science, School of Electrical Engineering and Computer Science, The University of Newcastle, Australia.

    Thesis Available

Book Chapter:

  1. Brankovic, L, Islam, M. Z. and Giggins H (2007): Privacy-Preserving Data Mining, Security, Privacy and Trust in Modern Data Management, Springer, Editors Milan Petkovic and Willem Jonker ISBN: 978-3-540-69860-9, Chapter 11, pg. 151-166.

    Pre-print Available

Refereed Journal Articles:

  1. Islam, M. Z., Estivill-Castro, V., Rahman, M. A. and Bossomaier, T. (2018): Combining K-Means and a Genetic Algorithm through a Novel Arrangement of Genetic Operators for High Quality Clustering, Expert Systems with Applications (ESWA), Vol. 91, pg. 402-417.
    (SJR Rank Q1, SJR H-Index 131, 2017 Impact Factor 3.928, Ranked #3 among Artificial Intelligence journals by Google Scholar)

    Pre-print Available

  2. Fletcher, S. and Islam, M. Z. (2017): Measuring Rule Retention in Anonymized Data - when one Measure is not Enough, Transactions on Data Privacy (TDP), Accepted on 10 October, 2017.

    With my PhD student during his PhD studies.

    Pre-print Available

  3. Adnan, M. N. and Islam, M. Z. (2017): Forest PA: Constructing a Decision Forest by Penalizing Attributes used in Previous Trees, Expert Systems with Applications (ESWA), Vol. 89, pg. 389 - 403, DOI: https://doi.org/10.1016/j.eswa.2017.08.002.
    (SJR Rank Q1, SJR H-Index 131, 2017 Impact Factor 3.928, Ranked #3 among Artificial Intelligence journals by Google Scholar)

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

    YouTube Video on Freely Available Software

  4. Fletcher, S. and Islam, M. Z. (2017): Differentially Private Random Decision Forests using Smooth Sensitivity, Expert Systems with Applications (ESWA), Vol. 78, pg. 16-31, DOI: http://dx.doi.org/10.1016/j.eswa.2017.01.034.
    (ISI Web of Knowledge Rank Q1, 2017 Impact Factor 2.981, SJR H-index 112, Ranked #1 among Artificial Intelligence journals by Google Scholar)

    With my PhD student during his PhD studies.

    Pre-print Available

  5. Adnan, M. N. and Islam, M. Z. (2017): ForEx++: A New Framework for Knowledge Discovery from Decision Forests, Australasian Journal of Information Systems (AJIS), Vol. 21, pg. 1-20, ISSN Online: 1326-2238 Hard copy: 1449-8618, DOI http://dx.doi.org/10.3127/ajis.v21i0.1694.

    With my PhD student during his PhD studies.

    (ABDC 2013 Rank A)

    Pre-print Available

  6. Islam, M. Z., D'Alessandro, S., Furner, M., Johnson, L., Gray, D. and Carter, L. (2016): Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia, Australasian Journal of Information Systems, pg. 1 - 17, Vol. 20, DOI: http://dx.doi.org/10.3127/ajis.v20i0.1420
    (ABDC 2013 Rank A, CORE Rank B)

    Pre-print Available

  7. Beg, A. H., Islam, M. Z., and Estivill-Castro. V. (2016): Genetic Algorithm with Healthy Population and Multiple Streams Sharing Information for Clustering, Knowledge-Based Systems, Vol. 114, pp. 61-78, ISSN 0219-1377, Springer London. doi: http://dx.doi.org/10.1016/j.knosys.2016.09.030 Available at Here
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence, SJR Rank Q1, 2016 H-index 63)

    With my PhD student during his PhD studies.

    Pre-print Available

  8. Adnan, M. N. and Islam, M. Z. (2016): Optimizing the Number of Trees in a Decision Forest to Discover a Subforest with High Ensemble Accuracy using a Genetic Algorithm, Knowledge-Based Systems, Vol. 110, pp. 86-97, ISSN 0219-1377, Springer London. doi: http://dx.doi.org/10.1016/j.knosys.2016.07.016 Available at Here
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence)

    With my PhD student during his PhD studies.

    Pre-print Available

  9. Bernoth, M., Burmeister, O. K., Morrison, M., Islam, M. Z., Onslow, F., and Cleary, M. (2016): The impact of a participatory care model on work satisfaction of care workers and the functionality, connectedness and mental health of community dwelling older people, Issues in Mental Health Nursing, Vol. 37, Issue 6, pp. 429-35, doi: 10.3109/01612840.2016.1149260.
    (SJR 2013 Rank Q1)

    Pre-print Available

  10. Thomas, C., Burmeister, O. K., Islam, M. Z., Dayhew, M., and Crichton, M. (2016): Client Welfare & Communication of Mental Health Data, Post Publication Review, Australasian Journal of Information Systems, Vol. 20, pg. 1-5.
    (ABDC 2013 Rank A)

  11. Rahman, M. G., and Islam, M. Z. (2016): Discretization of Continuous Attributes Through Low Frequency Numerical Values and Attribute Interdependency, Expert Systems with Applications (ESWA), Vol. 45, pp. 410-423, DOI: 10.1016/j.eswa.2015.10.005, 1 March, 2016.
    (ISI Web of Knowledge Rank Q1, 2014 Impact Factor 2.240, Ranked #1 among Artificial Intelligence journals by Google Scholar)

    With my PhD student during his PhD studies.

    Pre-print Available

  12. Siers, M., and Islam, M. Z. (2015): Software Defect Prediction Using a Cost Sensitive Decision Forest and Voting, and a Potential Solution to the Class Imbalance Problem, Information Systems, Vol. 51, pg. 62-71.
    (2013 Impact Factor 1.235, ERA 2010 Rank A*)

    With my Honours student during his Honours studies.

    Pre-print Available

  13. Rahman, M. G., and Islam, M. Z. (2015): Missing Value Imputation using a Fuzzy Clustering Based EM Approach, Knowledge and Information Systems, Vol. 46, Issue 2, pp. 389-422, ISSN 0219-1377, Springer London. doi: 10.1007/s10115-015-0822-y
    (ISI Web of Knowledge Rank Q1, 2013 Impact Factor: 2.639, ranked 21st out of 121 ranked journals in its category.)

    With my PhD student during his PhD studies.

    Pre-print Available

  14. Burmeister, O., Islam, M. Z., Dayhew, M., Crichton, M. (2015): Enhancing Client Welfare through Better Communication of Private Mental Health Data Between Rural Service Providers, Australasian Journal of Information Systems (AJIS), Vol. 19, pp. 1 -14, DOI: http://dx.doi.org/10.3127/ajis.v19i0.1206. ABDC 2013 Rank A. http://journal.acs.org.au/index.php/ajis/article/view/1206

    Pre-print Available

  15. Fletcher, S., and Islam, M. Z. (2015): An Anonymization Technique using Intersected Decision Trees , Journal of King Saud University - Computer and Information Sciences, Vol. 27, Issue 3, pp. 297 - 304, Elsevier.(Available on line at http://dx.doi.org/10.1016/j.jksuci.2014.06.015 )

    With my PhD student during his Honours studies.

    Pre-print Available

  16. Rahman, M. A., Islam, M. Z., and Bossomaier, T. (2015): ModEx and Seed-Detective: Two Novel Techniques for High Quality Clustering by using Good Initial Seeds in K-Means, Journal of King Saud University - Computer and Information Sciences, Vol. 27, Issue 2, pp. 113 - 128, doi:10.1016/j.jksuci.2014.04.002, Elsevier.

    With my PhD student during his PhD studies.

    Pre-print Available

  17. Fletcher, S., and Islam, M. Z. (2015): Measuring Information Quality for Privacy Preserving Data Mining, International Journal of Computer Theory and Engineering, Vol. 7, No. 1, pp. 21-28, February 2015, DOI: 10.7763/IJCTE.2015.V7.924. (Available here)

    With my PhD student during his PhD studies.

    Pre-print Available

  18. Al-Saggaf, Y., and Islam, M. Z. (2015): Data Mining and Privacy of Social Network Sites' Users: Implications of the data mining problem, Science and Engineering Ethics, Vol. 21, Issue 4, pp. 941-966, DOI 10.1007/s11948-014-9564-6, Springer, (available at Springer Link )
    (ERA Rank A, ISI Web of Science Rank Q1, ranked the 3rd out of 56 journals of its category of history and philosphy of science as on 15 Oct 2014, 2013 Impact Factor: 1.516)

    Pre-print Available

  19. Rahman, M. A., and Islam, M. Z. (2014): A Hybrid Clustering Technique Combining a Novel Genetic Algorithm with K-Means, Knowledge-Based Systems, Vol. 71, November 2014, pp. 345-365, DOI: 10.1016/j.knosys.2014.08.011, Available at http://dx.doi.org/10.1016/j.knosys.2014.08.011
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence)

    With my PhD student during his PhD studies.

    Pre-print Available

  20. Rahman, M. G., and Islam, M. Z. (2014): FIMUS: A Framework for Imputing Missing Values Using Co-appearance, Correlation and Similarity Analysis, Knowledge-Based Systems, Vol. 56, pp. 311-327, January 2014, DOI: 10.1016/j.knosys.2013.12.005 Available at http://dx.doi.org/10.1016/j.knosys.2013.12.005
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence)

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

  21. Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques, Knowledge-Based Systems, Vol. 53, pp. 51 - 65, ISSN 0950-7051, DOI information: 10.1016/j.knosys.2013.08.023, Available at http://www.sciencedirect.com/science/article/pii/S0950705113002591
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence)

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

  22. Al-Saggaf, Y., and Islam, M. Z. (2013): A Malicious Use of a Clustering Algorithm to Threaten the Privacy of a Social Networking Site User, World Journal of Computer Application and Technology, Vol. 1, Issue 2, pg. 29-34, DOI:10.13189/wjcat.2013.010202, Available at http://www.hrpub.org/download/201309/wjcat.2013.010202.pdf

    Pre-print Available

  23. Al-Saggaf, Y., and Islam, M. Z. (2012): Privacy in Social Network Sites (SNS) - the threats from Data Mining, Ethical Space: The International Journal of Communication Ethics, Vol. 9, Issue 4, pg. 32 - 40, ISSN 1742-0105. (available at http://journals.communicationethics.net/index.php )
    (ERA Rank A)

    Pre-print Available

  24. Islam, M. Z., and Brankovic, L.(2011): Privacy Preserving Data Mining: A Noise Addition Framework Using a Novel Clustering Technique, Knowledge-Based Systems Vol. 24, Issue 8, ISBN 0950-7051,(December 2011) pg. 1214-1223, (DOI: 10.1016/j.knosys.2011.05.011)
    (ABDC 2013 Rank A, ISI Web of Knowledge Rank Q1, 2012 Impact Factor: 4.104, the 6th best journal out of 115 ranked journals in its category of Computer Science, Artificial Intelligence)
    With my respected PhD supervisor.

    Pre-print Available

  25. Khan, M. A., Islam, M. Z., and Hafeez, M. (2011): Irrigation Water Requirement Prediction through Various Data Mining Techniques Applied on a Carefully Pre-processed Dataset, Journal of Research and Practice in Information Technology, Vol. 43, Issue 22, pp. 1-17, May 2011.
    (ERA 2010 Rank B)

    With a PhD student, who I co-supervised, during his PhD studies.

    Pre-print Available

  26. Zia, T. A., Al-Saggaf, Y., Islam, M. Z., Zheng, L., and Weckert, J. (2009): The Digital Divide in Asia: Cases from Yemen, Bangladesh, Pakistan and China, Journal of Information Ethics (JIE). Vol. 18, No. 2 (Fall 2009). McFarland & Company, Inc. Publishers, pg. 50 - 76.
    (ERA 2010 Rank B)

    Pre-print Available

Refereed Conference Proceedings:

  1. Reza, K. J., Islam, M. Z., and Estivill-Castro, V. (2017): Social Media Users' Privacy Against Malicious Data Miners, In Proc. of the 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017), Nanjing, Jiangsu, China, November 24-26, 2017. CORE 2017 Rank B. (Accepted 31 August, 2017)

    With my PhD student during his PhD studies.

  2. Adnan, M. N., and Islam, M. Z. (2017): Effects of Dynamic Subspacing in Random Forest, In Proc. of the 13th Advanced Data Mining and Applications (ADMA 2017), Singapore, November 5-6, 2017, Lecture Notes in Artificial Intelligence, Vol. 10604, ISSN 0302-9743, pg. 303-312, https://doi.org/10.1007/978-3-319-69179-4. CORE 2017 Rank B.

    Pre-print Available

  3. Siddiqui, M. K., Islam, M. Z. and Kabir, A. (2017): Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure, In Proc. of the 13th Advanced Data Mining and Applications (ADMA 2017), Singapore, November 5-6, 2017, Lecture Notes in Artificial Intelligence, Vol. 10604, ISSN 0302-9743, pg. 386-398, https://doi.org/10.1007/978-3-319-69179-4. CORE 2017 Rank B.

    With my PhD student during his PhD studies.

  4. Babar, Z., Islam, M. Z., and Mansha, S. (2017): Rank Forest: Systematic Attribute Sub-spacing in Decision Forest, In Proc. of the 15th Australasian Data Mining Conference (AusDM 2017), Melbourne, Australia, August 19-20, 2017. ERA 2010 Rank B, CORE 2017 Rank Australasian. (Accepted 10 July, 2017)

    Pre-print Available

  5. Reza, K., Islam, M. Z., and Estivill-Castro, V. (2017): 3LP: Three Layers of Protection for Individual Privacy in Facebook, In Proc. of the 32nd International Conference on ICT Systems Security and Privacy Protection (IFIP SEC 2017), Rome, Italy, May 29-31, 2017, pp. 108-123, DOI: 10.1007/978-3-319-58469-0_8. CORE 2017 Rank B. Conference Link. Acceptance rate 19.39% (38 papers from 196 submissions).

    With my PhD student during his PhD studies.

    Pre-print Available

  6. Islam, M. Z., Furner, M., and Siers, M. (2016): A Knowledge Discovery and Decision Support Tool for Efficient Dam Management, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016. ERA 2010 Rank B. (Accepted on 25 October, 2016)

    Pre-print Available

    YouTube Video Available

  7. Fletcher, S. and Islam, M. Z. (2016): Measuring the Similarity between Rule Lists, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016. ERA 2010 Rank B. (Accepted on 25 October, 2016)

    With my PhD student during his PhD studies.

    Pre-print Available

  8. Adnan, M. N. and Islam, M. Z. (2016): Knowledge Discovery from a Data Set on Dementia through Decision Forest, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016. ERA 2010 Rank B. (Accepted on 25 October, 2016)

    With my PhD student during his PhD studies.

    Pre-print Available

  9. Siers, M. and Islam, M. Z. (2016): Addressing Class Imbalance and Cost Sensitivity in Software Defect Prediction by Combining Domain Costs and Balancing Costs, In Proc. of the 12th International Conference on Advanced Data Mining and Applications (ADMA), Gold Coast, Australia, December 12 - 15, 2016, Lecture Notes in Artificial Intelligence (LNAI), pp. 156-171, Vol. 10086, ISBN 978-3-319-49586-6, DOI: 10.1007/978-3-319-49586-6. ERA 2010 Rank B, CORE 2014 Rank B.

    Published as a Spotlight Research Paper, where only 18 (17%) out of 105 submitted papers were accepted as spotlight research papers.

    With my PhD student during his PhD studies.

    Pre-print Available

  10. Adnan, M. N. and Islam, M. Z. (2016): On Improving Random Forest for Hard-to-Classify Records, In Proc. of the 12th International Conference on Advanced Data Mining and Applications (ADMA), Gold Coast, Australia, December 12 - 15, 2016, Lecture Notes in Artificial Intelligence (LNAI), pp. 558-566, Vol. 10086, ISBN 978-3-319-49586-6, DOI: 10.1007/978-3-319-49586-6. ERA 2010 Rank B, CORE 2014 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

  11. Siddiqui, M. K. and Islam, M. Z. (2016): Data Mining Approach in Seizure Detection, In Proc. of the IEEE TENCON 2016, Singapore, November 22 - 25, 2016, pg. 3579 - 3583, DOI: 10.1109/TENCON.2016.7848724, Electronic ISBN: 2159-3450. ERA 2010 Rank C.

    With my PhD student during his PhD studies.

  12. Beg, A. H. and Islam, M. Z. (2016): A Novel Genetic Algorithm-Based Clustering Technique and its Suitability for Knowledge Discovery from a Brain Dataset, In Proc. of the IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24 - 29, 2016, pp. 948- 956. DOI: 10.1109/CEC.2016.7743892, available here ERA 2010 Rank A, CORE 2014 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

  13. Beg, A. H. and Islam, M. Z. (2016): Novel Crossover and Mutation Operation in Genetic Algorithm for Clustering, In Proc. of the IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24 - 29, 2016, pp. 2114- 2121. DOI: 10.1109/CEC.2016.7744049, Available here ERA 2010 Rank A, CORE 2014 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

  14. Adnan, M. N. and Islam, M. Z. (2016): Forest CERN: A New Decision Forest Building Technique, In Proc. of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016, April 19-22, pp. 304 - 315, Part I, LNAI 9651, DOI: 10.1007/978-3-319-31753-3 25, ISBN 978-3-319-31752-6, Auckland, New Zealand.
    (ERA 2010 Rank A, CORE 2014 Rank A)

    With my PhD student during his PhD studies.

    Pre-print Available

    YouTube Video Available

  15. Beg, A. H. and Islam, M. Z. (2016): Branches of Evolutionary Algorithms and their Effectiveness for Clustering Records, In Proc. of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), Hefei, China, June 5 - 7, 2016. (Accepted on 6 April, 2016)
    ERA 2010 Rank A.

    Pre-print Available

  16. Beg, A. H. and Islam, M. Z. (2016): Advantages and Limitations of Genetic Algorithms for Clustering Records, In Proc. of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), Hefei, China, June 5 - 7, 2016. (Accepted on 6 April, 2016)
    ERA 2010 Rank A.

    Pre-print Available

  17. Siers, M. and Islam, M. Z. (2016): RB Clust: High quality class-specific clustering using rule-based classification, In Proc. of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, April 27 - 29, 2016, pg. 593 - 598. ERA 2010 Rank B, CORE 2014 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

  18. Beg, A. H. and Islam, M. Z. (2016): Genetic Algorithm with Novel Crossover, Selection and Health Check for Clustering, In Proc. of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, April 27 - 29, 2016, pg. 575 - 580. ERA 2010 Rank B, CORE 2014 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

  19. Fletcher, S. and Islam, M. Z. (2015): A Differentially-Private Random Decision Forest using Reliable Signal-to-Noise Ratios, In Proc. of the 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Canberra, Australia, 30 November - 4 December, 2015, Lecture Notes in Computer Science (LNCS), pp. 192-203, Vol. 9457, ISBN 978-3-319-26350-2, DOI: 10.1007/978-3-319-26350-2_17, ERA 2010 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

  20. Siers, M. and Islam, M. Z. (2015): Standoff-Balancing: A Novel Class Imbalance Treatment Method Inspired by Military Strategy, In Proc. of the 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Canberra, Australia, 30 November - 4 December, 2015, Lecture Notes in Computer Science (LNCS), pp. 517 - 525, Vol. 9457, ISBN 978-3-319-26350-2, DOI: 10.1007/978-3-319-26350-2_46, ERA 2010 Rank B.

    With my PhD student during his PhD studies.

    Pre-print Available

  21. Fletcher, S. and Islam, M. Z. (2015): A Differentially Private Decision Forest, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 99- 108, ISBN 978-1-921770-18-0.
    ERA 2010 Rank B

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Pre-print Available

  22. Adnan, M. N. and Islam, M. Z. (2015): Complement Random Forest, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 89- 98, ISBN 978-1-921770-18-0.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Pre-print Available

  23. Furner, M. and Islam, M. Z. (2015): Multiple Imputation on Partitioned Datasets, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 59- 68, ISBN 978-1-921770-18-0.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my Honours student based on his Honours studies.

    Pre-print Available

  24. Rahman, M. A. and Islam, M. Z. (2015): AWST: A Novel Attribute Weight Selection Technique for Data Clustering, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 51- 58, ISBN 978-1-921770-18-0.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student based on his PhD studies.

    Pre-print Available

  25. Beg, A. H., and Islam, M. Z. (2015): Clustering by Genetic Algorithm - High Quality Chromosome Selection for Initial Population, In Proc. of the 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015), Auckland, New Zealand, 15 -17 June, 2015, pg. 129 - 134. (ERA 2010 Rank A).

    With my PhD student during his PhD studies.

    Pre-print Available

  26. Adnan, M., and Islam, M. Z. (2015): One Vs All Binarization Technique in the Context of Random Forest, In Proc. of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp. 385 - 390, Bruges, Belgium, April 22 - 24, 2015 (CORE 2014 Rank B).

    With my PhD student during his PhD studies.

    Pre-print Available

  27. Adnan, M., and Islam, M. Z. (2015): Improving the Random Forest Algorithm by Randomly Varying the Size of the Bootstrap Samples for Low Dimensional Data Sets, In Proc. of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp. 391 - 396, Bruges, Belgium, April 22 - 24, 2015 (CORE 2014 Rank B).

    With my PhD student during his PhD studies.

    Pre-print Available

  28. Adnan, M., and Islam, M. Z. (2014): A Comprehensive Method for Attribute Space Extension for Random Forest, In Proc. of the 17th International Conference on Computer and Information Technology (ICCIT 14), pp. 25-29, 22-23 December, Dhaka, Bangladesh. (ERA 2010 Rank C).

    With my PhD student during his PhD studies.

    Pre-print Available

  29. Burmeister, O., Islam, M. Z., Dayhew, M., Crichton, M. (2014): Interagency Communication of Private Mental Health Data, In Proc. of the 25th Australasian Conference on Information Systems (ACIS 2014), Auckland, New Zealand, 8-10 December, 2014. (ERA 2010 Rank A, CORE 2014 Rank Australasian)
    Conference Link
    Paper available here
    Received Best Paper Award


    Pre-print Available

  30. Islam, M. Z., Mamun, Q., and Rahman, M. G. (2014): Data Cleansing During Data Collection from Wireless Sensor Networks, In Proc. of the 12th Australasian Data Mining Conference (AusDM 2014), Brisbane, Australia, 27-28 November, 2014. CRPIT, Vol. 158, pp. 195- 203, ISBN 978-1-921770-17-3.
    (ERA 2010 Rank B, CORE 2014 Rank Australasian)

    ( The paper is available here.)

    Pre-print Available

  31. Adnan, M., and Islam, M. Z. (2014): ComboSplit: Combining Various Splitting Criteria for Building a Single Decision Tree, In Proc. of the International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2014), Kuala Lumpur, Malaysia, 17-19 November, 2014, pp 1-8, ISBN 978-1-941968-02-4.(CORE 2014 Rank C) Conference Link

    With my PhD student during his PhD studies.

    Pre-print Available

  32. Xiang, Z., and Islam, M. Z. (2014): Hartigan's Method for K-Mode Clustering and its Advantages, In Proc. of the 12th Australasian Data Mining Conference (AusDM 2014), Brisbane, Australia, 27-28 November, 2014. CRPIT, Vol. 158, pp. 25- 30, ISBN 978-1-921770-17-3.
    (ERA 2010 Rank B, CORE 2014 Rank Australasian)

    ( The paper is available here.)

    Pre-print Available

  33. Siers, M., and Islam, M. Z. (2014): Cost Sensitive Decision Forest and Voting for Software Defect Prediction, In Proc. of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2014), pp. 929 - 936, LNAI 8862, Gold Coast, Australia, 1-5 December, 2014. (CORE 2014 Rank B) Conference Link

    With my Honours student during his Honours studies.

    Pre-print Available

  34. Xiang, Z., and Islam, M. Z. (2014): The Performance of Objective Functions for Clustering Categorical Data, In Proc. of the 2014 Pacific Rim Knowledge Acquisition Workshop (PKAW 2014), pp. 16 - 28, LNCS 8863, DOI: 10.1007/978-3-319-13332-4_2, ISBN 978-3-319-13331-7, Gold Coast, Australia, 1- 2 December, 2014.(CORE 2014 Rank B) Conference Link

    Pre-print Available

  35. Estivill-Castro, V., Hough, P., and Islam, M. Z. (2014): Empowering Users of Social Networks to Assess Their Privacy Risks, In Proc. of the IEEE International Conference on Big Data (IEEE BigData 2014), Washington DC, USA, 27-30 October 2014, pg. 644-649, ISBN 978-1-4799-5666-1 and ISBN 978-1-4799-5665-4. Conference Link

    With my Honours student and a colleague. Author names are in alphabetic order.

    Pre-print Available

  36. Fletcher, S., and Islam, M. Z. (2014): Quality Evaluation of an Anonymized Dataset, In Proc. of the 22nd International Conference on Pattern Recognition (ICPR 2014), pg. 3594-3599, DOI 10.1109/ICPR.2014.618, Stockholm, Sweden, 24-28 August 2014. (ERA 2010 Rank B, CORE 2014 Rank B)

    With my PhD student during his PhD studies.

    Pre-print Available

  37. Adnan, M., Islam, M. Z., and Kwan, P. (2014): Extended Space Decision Tree, In Proc. of the 13th International Conference on Machine Learning and Cybernetics (ICMLC 2014), pp. 219 - 230, DOI: 10.1007/978-3-662-45652-1_23, Lanzhou, China, 13-16 July, 2014. (Best Paper Award Nomination)(ERA 2010 Rank C)

    With my PhD student during his PhD studies.

    Pre-print Available

  38. Rahman, M. G., and Islam, M. Z. (2014): iDMI: A Novel Technique for Missing Value Imputation using a Decision Tree and Expectation-Maximization Algorithm, In Proc. of the 16th International Conference on Computer and Information Technology (ICCIT 14), Khulna, Bangladesh, pp. 496-501, 08-10 March 2014.(CORE 2014 Rank C)

    With my PhD student during his PhD studies.

  39. Rahman, M. A., Islam, M. Z. and Bossomaier, T. (2014): DenClust: A Density Based Seed Selection Approach for K-Means, In Proc. of the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2014), pg. 784-795, Part II, Lecture Notes in Computer Science, Vol. 8468, Springer International Publishing Switzerland, L. Rutkowski et al. Eds., Zakopane, Poland, 1-5 June 2014. (Available from http://link.springer.com/chapter/10.1007%2F978-3-319-07176-3_68)

    With my PhD student during his PhD studies.

    Pre-print Available

  40. Rahman, M. G., and Islam, M. Z. (2013): kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set, In Proc. of the 9th International Conference on Advanced Data Mining and Applications(ADMA 13), Hangzhou, China, 14-16 December 2013, pg. 250-263, DOI: 10.1007/978-3-642-53917-6, ISSN: 0302-9743, H. Motoda et al. (Eds.). (ERA 2010 Rank B)

    With my PhD student during his PhD studies.

    Pre-print Available

  41. Rahman, M. G., and Islam, M. Z. (2013): A Novel Framework Using Two Layers of Missing Value Imputation, In Proc. of the 11th Australasian Data Mining Conference (AusDM 13), Canberra, Australia, 13-15 November 2013. CRPIT, Vol. 146, pp. 149- 160. ISBN 978-1-921770-16-6.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Received Best Paper Award

    Pre-print Available

  42. Rahman, M. G., and Islam, M. Z. (2013): Data Quality Improvement by Imputation of Missing Values, In Proc. of the 2013 International Conference on Computer Science and Information Technology, Yogyakarta, Indonesia, 16 - 18 June, 2013, pg. 82-88, ISBN: 978-979-3812-20-5.

    With my PhD student during his PhD studies.

    Pre-print Available

  43. Rahman, M. A., and Islam, M. Z. (2012): CRUDAW: A Novel Fuzzy Technique for Clustering Records Following User Defined Attribute Weights, In Proc. of the 10th Australasian Data Mining Conference (AusDM 12), Sydney, Australia. December 4 - 7, 2012, CRPIT, Vol. 134, Zhao, Y., Li, J., Kennedy, P., and Christen, P. Eds., ACS, pg. 27 - 42.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Pre-print Available

  44. Khan, M. A., Islam, M. Z., and Hafeez, M. (2012): Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement, In Proc. of the 10th Australasian Data Mining Conference (AusDM 12), Sydney, Australia. December 4 - 7, 2012, CRPIT, Vol. 134, Zhao, Y., Li, J., Kennedy, P., and Christen, P. Eds., ACS, pg. 199 - 208.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With a PhD student, who I co-supervised, during his PhD studies.

    Pre-print Available

  45. Bossomaier, T., Islam, M. Z., Duncan, R., and Gao, J. (2012): Simulation of House Prices for Improved Land Valuation, In Proc. of the 24th European Modeling & Simulation Symposium (EMSS 12), Vienna, Austria, September 19 - 21, 2012, pp. 1-7, DIME University of Genoa, ISBN/ISSN 978-88-97999-01-0.

    Pre-print Available

  46. Rahman, M. G., Islam, M. Z., Bossomaier, T., and Gao, J. (2012): CAIRAD: A Co-appearance based Analysis for Incorrect Records and Attribute-values Detection, In Proc. of IEEE International Joint Conference on Neural Networks (IJCNN 12), Brisbane, Australia. June 10 - June 15, 2012, pg. 2190-2199.(ERA 2010 Rank A)

    With my PhD student during his PhD studies.

    Pre-print Available

  47. Islam, M. Z. and Giggins, H. (2011): Knowledge Discovery through SysFor: A Systematically Developed Forest of Multiple Decision Trees, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011. CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 195-204.
    ERA 2010 Rank B.

    ( The paper is available here.)

    Pre-print Available

    YouTube Video Available

    YouTube Video on Freely Available Software

  48. Rahman, M. G. and Islam, M. Z. (2011): A Decision Tree-based Missing Value Imputation Technique for Data Pre-processing, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 41-50.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Pre-print Available

  49. Rahman, M. A. and Islam, M. Z. (2011): Seed-Detective: A Novel Clustering Technique Using High Quality Seed for K-Means on Categorical and Numerical Attributes, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 211-220.
    ERA 2010 Rank B.

    ( The paper is available here.)

    With my PhD student during his PhD studies.

    Pre-print Available

  50. Khan, M. A., Islam, M. Z., and Hafeez, M. (2011): Irrigation Water Demand Forecasting - A Data Pre-processing and Data Mining Approach Based on Spatiotemporal Data, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 183-194.
    ERA 2010 Rank B.

    ( The paper is available here.)

    ( the Best Student Paper Award )

    With a PhD student, who I co-supervised, during his PhD studies.

    Pre-print Available

  51. Islam, M. Z. (2010): EXPLORE: A Novel Decision Tree Classification Algorithm, In Proc. of the 27th British National Conference on Databases (BNCOD 2010), LNCS Vol. 6121, Data Security and Security Data, Springer, Berlin/Heidelberg (2012), L.M. MacKinnon (Ed.) ISBN 978-3-642-25703-2, June 29- July 01, 2010, Dundee, Scotland. L.M. MacKinnon (Ed.) 55-71. (ERA 2010 Rank B)

    Pre-print Available

    YouTube Video Available

  52. Zia, T.A., and Islam, M. Z. (2010): Communal Reputation and Individual Trust (CRIT) in Wireless Sensor Networks, In Proc. of the 5th International Conference on Availability, Reliability and Security (ARES 2010), Published by IEEE Computer Society, February 15-18, 2010, Krakow, Poland, 347-352. (ERA 2010 Rank B)

    Pre-print Available

  53. Islam, M. Z. and Brankovic, L (2005): DETECTIVE: A Decision Tree Based Categorical Value Clustering and Perturbation Technique in Privacy Preserving Data Mining, In Proc. of the 3rd International IEEE Conference on Industrial Informatics (INDIN 2005), 10-12 August, Perth, Australia.
    With my respected PhD supervisor.

    Pre-print Available

  54. Alfalayleh, M., Brankovic, L., Giggins, H and Islam, M. Z. (2004): Towards the Graceful Tree Conjecture: A Survey, In Proc. of AWOCA 2004, 7-9 July, Ballina, Australia. (ERA 2010 Rank B)

    Author names are in alphabetic order.

    Pre-print Available

  55. Islam, M. Z. and Brankovic, L. (2004): A Framework for Privacy Preserving Classification in Data Mining,In Proc. of Australasian Workshop on Data Mining and Web Intelligence (DMWI 2004), Dunedin, New Zealand, CRPIT, 32, Purvis, M., Ed. ACS, 163-168.
    With my respected PhD supervisor.

    Pre-print Available

  56. Islam, M. Z., Barnaghi, P. M. and Brankovic, L.(2003): Measuring Data Quality: Predictive Accuracy vs.Similarity of Decision Trees, In Proceedings of the 6 th International Conferenceon Computer & Information Technology (ICCIT 2003), Dhaka, Bangladesh, Vol. 2, 457-462. (ERA 2010 Rank C)
    With my respected PhD supervisor.

  57. Islam, M. Z. and Brankovic, L. (2003): Noise Addition for Protecting Privacy in Data Mining, In Proceedings of the 6 th Engineering Mathematics and Applications Conference (EMAC 2003), Sydney, Australia, 85-90 .
    With my respected PhD supervisor.

    Pre-print Available

Please feel free to contact me if you are interested to have a look at any of these published papers.

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Recent Research Projects

External Funding from Murrumbidgee Local Health District (MLHD), NSW Health, Australia. Period 2016 - 2016. Project Title: Patient Risk Stratification Project, CIs: Zahid Islam and Mark Morrison - $51,677. CSU Research Office Project Reference number: 0000101620.

  • We apply data mining techniques to develop tools for effective health management.

External Funding from the Department of Social Services, Australia. Period 2015 - 2017. Project Title: Social and community links - a driver of healthy and active ageing, CIs: Oliver Burmeister (Lead CI), Mark Morrison, Zahid Islam, Maree Bernoth, Rylee Dionigi, and Rahena Akhter - $655,000 ($60,000 to CSU). Partner organisation: Carewest Pty Ltd, Orange, NSW. CSU Research Office Project Reference number: 0000101130.

  • A component of the research project is the use of data mining and data analytics to discover hidden knowledge related to various research issues on healthy ageing. Besides, data mining techniques will also be used to evaluate the effectiveness of proposed intervention techniques.

External Funding from Young and Well Cooperative Research Centre 2014. Project Title: Synergy ecosystem data storage: medico-legal and ethical challenges. CIs: Dr Oliver Burmeister, Dr Zahid Islam, Dr Maree Burnoth and Ms Carli Kulmar - $16,500.

  • The Young and Well (Y&W) Collaborative Research Centre (CRC) has received $5 million for an innovative new product called the Synergy Ecosystem. The Synergy Ecosystem is being rolled out progressively, beginning with hAppiness Central, a university-based online wellbeing resource run through the student services portal. This project addresses the medical, legal, ethical and privacy challenges associated with data collection, storage, sharing and analysis for the ES, focusing on the first product, but also addressing the longer term needs of the Synergy Ecosystem . The project addresses the data security, ownership, privacy, integrity, and management including reporting and access.

External Fundign from Hobart District Nursing 2013. Project Title: Review of Support Worker Integration with Functional Decline, CIs: Prof Mark Morrisson, Dr Maree Bernoth, Dr Oliver Burmeister, and Dr. Zahid Islam - $39,400.

  • The main aim of this project is to suggest reasons and possible remedies of the functional decline for the employess and care receivers of Hobart District Nursing. The project also suggests and assesses some intervention plans. We apply data mining and other data analyses on the collected data in order to evaluate the effectiveness of the proposed interventions.

External Funding from Carewest, Australia 2013. Project Title: Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community. CIs: Prof Mark Morrison, Dr Oliver Burmeister, Dr Zahid Islam, Dr Ramudu Bhanugopan and Dr Maree Bernoth - $25,000.

  • This research seeks to investigate whether improved training and use of technology by clinicians (support workers) and training of volunteers improves human resource management outcomes among employees and volunteer carers who are involved in reducing the rate of functional decline among seniors. This research involves the use of experiments and pre-post surveys of subjects. Various data mining techniques are applied on the survey data for extracting the patterns and information in order to evaluate the impact of various interventions.

COMPACT Funding 2014, Project Tile: Development and Applicaiton of Domain Specific Data Mining Techniques to Predict and Explore the Brand Switching Tendency of Mobile Phone Users, CIs: Dr Zahid Islam and Prof Steven D'Alessandro - $11,236.26.

  • In this project we propose and use novel data mining algorithms to analyse Brand Switching Tendency of Mobile Phone Users.

COMPACT Funding 2014, Project Tile: Industry Funding, CIs: Dr Zahid Islam and Prof Junbin Gao - $13,108.80.

  • In this project we accomplish the tasks for our industry partners.

Automatic and Natural Clustering of Records ($9124 Faculty COMPACT Fund 2012): Dr Islam, Prof Bossomaier, Prof Estivill-Castro, A/Prof Brankovic

  • In this study we aim to further improve our clustering techniques in order to group records in more meaningful clusters with automatic cluster number selection, attribute weights for clustering and so on. Clustering results will also be ­validated by various existing evaluation techniques and novel evaluation techniques.

Data Cleansing and Data Pre-processing Techniques($18,249 Faculty COMPACT Fund 2011): Dr Islam, Prof Bossomaier, Prof Gao

  • In this study we develop novel data cleansing techniques for improving data quality. The improved data will then be used in making better decision for an organisation. We have also developed an Agent Based Modeling (ABM) which is powered by data mining techniques in order to simulate the property market and thereby predict and assess property prices. We are also developing various techniques for more acceptable property valuation.

Data Mining threats on Privacy of Social Network Site (SNS) users ($7,000 Faculty COMPACT Fund 2011): Dr Al-Saggaf, Dr Islam

  • The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users.

Novel Data Mining Techniques for an Intelligent Business Decision Support System ($10,000 CRiCS Seed Grant 2010): Dr Islam

  • Due to the recent development of information processing technology and storage capacity businesses typically collect huge amount of data nowadays. Data are not useful unless necessary information is extracted from them. Various data mining tasks including data cleaning, classification, clustering, and prediction are usually applied on collected data for knowledge discovery.

    In this project we are developing an intelligent Decision Support System (DSS) that will integrate available data of an organisation by automatically extracting information from textual data through text data mining, detecting and correcting any corrupt (incorrect) data, and imputing all missing values. Our intelligent decision support system will be capable of grouping similar records, extracting multiple sets of patterns (instead of a single set of patterns or logic rules) through classification, and predicting future with very high accuracy. While grouping similar records our DSS will give a user huge flexibility to assign different weights on different attributes and thereby experience different groups of similar records in order to explore various patterns. The DSS will also provide a classification algorithm for extracting interesting patterns that are generally ignored by existing algorithms. For faster analysis of huge amount of real time data, our DSS will use GPGPU for parallel processing of our algorithms. GPU (instead of CPU) can process independent calculations in parallel using a single kernel on many records requiring similar calculations. We will use CUDA architecture for parallel computation.

    As a result the DSS can be used in various purposes such as diagnosis and prevention of diseases, behaviour analysis of equipments and predict any future breakdown, making decision on bank loan applications, and identification of suspicious tax returns - just to name a few.

A Novel Decision Tree Classification Algorithm ($6,000 CSU Small Grant 2009):Dr Islam

  • The aim of this project is to develop a novel decision tree algorithm that will extract useful patterns (that are currently ignored by existing algorithms) from a data set. We study various existing classification algorithms such as Decision tree algorithms, Neural networks, Bayesian algorithms and Genetic algorithms. We propose some modifications to existing algorithms and test the algorithm by applying it on a number of data sets. We compare the efficiencies of the proposed algorithm with various existing algorithms such as See 5, J48, and REPTree. The efficiencies are evaluated based on quality of extracted pattern, simplicity of the trees, Performance and Significance of logic rules. Our initial experimental results are very encouraging.

    These patterns can then be used in our original noise addition techniques and similarity evaluation of categorical values. My current Seed Grant project (2009) is for developing clustering technique where as the aim of this Small Grant project is to develop a classification technique. Although these two studies are significantly different to each other, they will both be used in our noise addition framework.

A Novel Clustering Technique for Categorical and Numerical Values ($3,000 Faculty of Business Seed Grant 2009): Dr Islam

  • Advances in information processing technology and storage capacity have enabled collection of huge amount of data for various data analyses. Data mining techniques such as classification are often applied on these data to extract hidden information. During the whole process of data mining these data get exposed to several parties which can potentially lead to breaches of individual privacy.

    During our previous research, we have presented a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining a high data quality. We added noise to all attributes, both numerical and categorical. Noise addition techniques developed for numerical attributes are not suitable for noise addition to categorical attributes, due to the absence of natural ordering in categorical attributes. Therefore, we presented a novel technique for clustering categorical values, and used it for noise addition to those values. We also proposed an extension of the technique for clustering records (not just attribute values) having categorical and/or numerical attributes. Our initial experiments indicate suitability of these techniques in clustering categorical values and in noise addition to these values. However, the clustering techniques (specially the one for clustering records) need to be sufficiently experimented and improved. They need to be compared with existing techniques in order to demonstrate their advantages.

    The aim of this project is to carry out extensive experiments to evaluate the performance of the proposed clustering techniques by comparing them with existing techniques such as CACTUS, ROCK and QROCK. The study also aims to improve the proposed clustering techniques. Their usefulness for being used in the noise addition framework will also be evaluated through the data quality and level of privacy in a perturbed data set.

A Privacy Preserving Data Mining Technique (Faculty of Business Research Assistants Support for Honours Students):Dr Islam

  • Due to the development of data collection and storage facilities, organisations these days collect a huge amount of data in almost every sector of life. Collected data (such as a patient dataset of a hospital) typically contain sensitive individual information. While the data can be useful in research, business analysis, decision making and prediction an organisation (such as a hospital) often is restricted to share and disclose the collected data due to potential breach of individual privacy. This restriction limits the usefulness of sophisticated data mining techniques in exploring patterns and other hidden information (such as causes of a disease and possible prevention techniques) from the collected data. We aim to develop techniques to ensure the privacy of data subjects while releasing the data for sharing among interested parties for knowledge discovery.

    We previously presented a complete framework for noise addition to all attributes of a data set in order to protect individual privacy in a data set while maintaining its original data quality. That is, we added noise to both numerical and categorical attributes in such a way so that the original patterns are preserved in a perturbed data set. We also presented an extended framework, which is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters including correlations among attributes of a data set. Thus the perturbed data set can be used not only for classification but also for other statistical analysis.

    We tested the framework and extended framework on different data sets, and compared our frameworks with some other noise addition techniques, through data quality of perturbed data sets. Data quality of a perturbed data set was evaluated through a few parameters such as the similarities of decision trees obtained from the original data set and the perturbed data set, prediction accuracy of a decision tree obtained from the perturbed data set, and the correlation matrices produced from the perturbed data set and the original data set. We also presented a technique for security analysis of a data set. An initial experiment indicates the existence of higher level of security in a data set perturbed by our framework than the security level in the original data set.

    In this project we are working on further improvement of our noise addition techniques and security analysis.

Privacy Preserving Multi-party Data Mining

  • Often multiple parties (such as the Commonwealth Bank and the Westpac Bank) intend to perform data mining on their combined dataset in order to extract more useful knowledge than the knowledge that can be extracted through data mining on their individual datasets separately. However, they typically hesitate to release their datasets to other parties due to the obligations to their customers for preserving individual privacy. Moreover, a party may not also want to disclose sensitive rules (such as they never approve loans to a particular group of people like the residents of a suburb simply due to the fact that they live there) to other parties. Such a disclosure can easily damage their reputation and image publicly. In this project we aim to develop techniques that will help the parties to effectively mine their combined data while preserving individual privacy and sensitive rules.

SysFor: A Systematically Developed Forest of Multiple Trees: Dr Islam

  • Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set, and to predict the class attribute value of an unlabeled record. Often a set of decision trees, rather than just a single tree, are also generated from a data set. The set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. In this project we present a novel technique for building a set of multiple trees called SysFor. Our initial experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery. Moreover, it also has higher prediction accuracy than a couple of existing champion techniques.

I am interested in group research. Please feel free to contact me if you are interested. Potential PhD students are also encouraged to contact me at my email address (zislam@csu.edu.au).


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Latest News

Awards.

Our Predictive Risk Stratification Tool for reducing avoidable hospital readmission for Murrumbidgee Local Health District of the NSW Health has received an Innovation Award in 2017 from the Agency for Clinical Innovation (ACI). See an Award Photo and CSU News.

We have again received the Faculty of Business Research Supervision Excellence Award 2016 in recognition of the achievements in supervising high quality PhD students at Charles Sturt University.

We have received the Research Excellence Award 2015 in the School of Computing and Mathematics, Charles Sturt University.

We have won the Subject Team Award 2015 in the Faculty of Business for wonderful team teaching in ITC106 and ITC558. The team teaching was led by Dr Zahid Islam as the convenor of the subjects. Other team members are Dr Anisur Rahman, Dr Ken Eustace, Dr Xiaodi Huang, Dr Recep Ulusoy and Dr Sudath Heiyanthuduwage.

We have received the Faculty of Business Research Supervision Excellence Award 2014 in recognition of the achievements in supervising high quality PhD students at Charles Sturt University.

We have received the Research Excellence Award 2013 in the School of Computing and Mathematics, Charles Sturt University.

We have received the Best Paper Award in the 25th Australasian Conference on Information Systems (ACIS 2014)in Auckland, New Zealand.(ERA 2010 Rank A).

We have received the Best Paper Award in the Eleventh Australasian Data Mining & Analytics Conference (AusDM 2013) in Canberra, Australia.

We have received the Best Student Paper Award in the Ninth Australasian Data Mining Conference (AusDM 2011) in Ballarat, Australia for our paper with a PhD student.

Faculty of Business Award 2014.
Faculty of Business Research Supervision Excellence Award 2014.
Faculty of Business Research Award 2015.
Faculty of Business Research Award 2014.
SCM Research Excellence Award 2015.
SCM Research Excellence Award 2015.
Faculty Research Supervision Excellence Award 2016
Faculty Research Supervision Excellence Award 2016.
ACI MLHD Innovation Award 2017.
Agency for Clinical Innovation (ACI) Award 2017.
ACI MLHD Innovation Award 2017.
Agency for Clinical Innovation (ACI) Award 2017.
Visits of our research collaborators.

Our research group regularly invites collaborators and researchers from all over the places. Recently Prof Vladimir Estivill-Castro of Griffith University and A/Prof Ljiljana Brankovic of Newcastle University visited us.

Prof Vlad Estivill-Castro discussing community partitioning to our serious PhD students, in 2013
Prof Vlad Estivill-Castro discussing community partitioning to our serious PhD students, in 2013.
Research is more like a fun when we do it in a hard working group.
Research is more like a fun when we do it in a hard working group.
A wonderful moment with my PhD supervisor and a few PhD students, in 2012
Prof Ljiljana Brankovic visited us in Bathurst, 2012.
Visitors from China
A group of three Professors from China and Queensland visited us on 18 October, 2016.
Invited Talks in Various Universities.

We regularly give invited talks in various universities. For example, I have recently given invited talks at seminars in the University of New England, Australia, Independent University of Bangladesh (IUB), Charles Sturt University, Australia and Deakin University, Burwood Campus at Melbourne, Australia.

Invited talk at the Independent University of Bangladesh, 2014.
Invited talk at the Independent University of Bangladesh in 2014.
Invited talk at VICPU, 2015.
Invited talk at VICPU, 2015.
Attendeding Various Conferences on Data Mining

Members of our group regularly attend high quality international conferences. For example, in 2013 we attended ADMA 13 in Hangzhou, China, AusDM 13 in Canberra Australia, in 2012 we attended AusDM 2012, IJCNN 2012, and ACSW 2012.

A wonderful moment with my PhD students in AusDM 2012
A wonderful moment with my PhD students in AusDM 2012 at Sydney
A Relaxed afternoon with the Data Mining Research Group at CSU after my Seminar in April, 2015
A Relaxed afternoon with the Data Mining Research Group after my Seminar at CSU in April, 2015.
On behalf of Mahmood Khan I am receiving the best student paper award for our paper with Mahmood at AusDM 2011
On behalf of Mahmood Khan I am receiving the best student paper award for our paper with Mahmood at AusDM 2011 at Ballarat
My talk on our novel Decision Forest technique at AusDM 2011
My talk on our novel Decision Forest technique at AusDM 2011 at Ballarat
With my PhD students at AusDM 2011
With my PhD students at AusDM 2011
A happy research team. A very nice moment at AusDM 2011
A happy research team. A very nice moment at AusDM 2011 at Ballarat

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We are uploading the source code of some of our papers. The programs are free software. You can redistribute them and/or modify them under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Algorithm Paper Title
Authors Citation
Code
SysFor Knowledge discovery through SysFor: a systematically
developed forest of multiple decision trees

This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.
Md Zahidul Islam,
Helen Giggins
BibTeX Java
ForestPA Forest PA: Constructing a Decision Forest by Penalizing Attributes used in Previous Trees

This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.
Md Nasim Adnan, and
Md Zahidul Islam
BibTeX Java
CSForest Cost sensitive decision forest and voting for software defect prediction Michael J. Siers, and
Md Zahidul Islam
BibTeX Java
Software Defect Prediction using a cost sensitive decision forest and voting,
and a potential solution to the class imbalance problem
Michael J. Siers, and
Md Zahidul Islam
BibTeX
Standoff-Balancing Standoff-Balancing: A novel class imbalance treatment method
inspired by military strategy
Michael J. Siers, and
Md Zahidul Islam
BibTeX Java
  1. Please cite the relevant papers as recorded in the above table if you use any of the above code either from this web page or any other web pages or WEKA.
  2. The CSForest code and Standoff-Balancing code are also available at Michael J. Siers' webpage.

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