Assoc Prof Kai Ming Ting - Researcher Profile

Kai Ming Ting

Address

Gippsland School of Information Technology
Building 4N, Gippsland Campus

Contact Details

Tel: +61 3 990 32847

Tel: +61 3 990 26241

Email: KaiMing.Ting@monash.edu


Biography

After receiving his PhD from the University of Sydney, Australia, Kai Ming Ting worked at the University of Waikato, New Zealand and Deakin University, Australia. He joined Monash University in 2001 and currently serves as the Associate Dean Research Training in Faculty of Information Technology and an Associate Professor in Gippsland School of Information Technology at Monash University. He had previously held visiting positions at Osaka University, Japan, Nanjing University, China, and Chinese University of Hong Kong.

His current research interests are in the areas of mass estimation and mass-based approaches, ensemble approaches, data stream data mining, and swarm intelligence. He is an associate editor for Journal of Data Mining and Knowledge Discovery. He co-chaired the Pacific-Asia Conference on Knowledge Discovery and Data Mining 2008. He has served as a member of program committees for a number of international conferences including ACM SIGKDD, IEEE ICDM and ICML. His research projects are supported by grants from Australian Research Council, US Air Force of Scientific Research (AFOSR/AOARD), and Australian Institute of Sport. Awards received include the Runner-up Best Paper Award in 2008 IEEE ICDM, and the Best Paper Award in 2006 PAKDD.

Current Research:

  • Mass estimation and mass-based approach
    • Mass estimation is a new base modelling mechanism in data mining, which serves as an alternative to density estimation for data modelling. It models data distribution in terms of mass distribution rather than density distribution. Mass estimation has been applied to anomaly detection, content-based information retrieval, clustering and regression to date, with a superior performance than existing non-mass-based methods in terms of efficiency and efficacy.
    • See MLJ 2012 paper for its fundamental idea [Software download] [Slides]
    • Multi-Dimensional Mass Estimation and Mass-based Clustering (IEEE ICDM 2010) [Slides]
    • Fast Anomaly Detection for Streaming Data (IJCAI 2011)
    • Information retrieval based on mass estimation (Pattern Recognition). This is the first information retrieve method which does not employ any metric to provide a ranking. It has been shown to perform better than existing metric-based methods in both image and music retrieval problems.
    • Density estimation based on mass (IEEE ICDM 2011). This is the first density estimation method which does not use any distance measure. [Software download]
    • Other related papers: Isolation Forests-see TKDD 2012 paper, ReFeat, SCiForest
    Ensemble methods
    • Feature-Subspace Aggregating (Feating): This is a generic ensemble approach that can enhance the predictive accuracy of both stable and unstable learners; unlike existing ensemble methods such as boosting and bagging which apply to unstable learners only. In particular, Feating makes feasible SVM ensembles that would otherwise be infeasible in large data sets. (Paper retrieved from Machine Learning Journal) [Software download] [Slides]
    Data mining tasks: anomaly detection, classification, clustering, music retrieval, image retrieval

Related Links:

Research & Supervision Interests

    Awards:

    Scholarships for research students:

          Scholarships funded by US Air Force Research Laboratory are available for projects related to mass estimation and mass-based approaches.

Keywords

Information retrieval, data mining, machine learning

Qualifications

Graduate Certificate of Higher Education
Institution: Monash University
Year awarded: 2004
Ph.D, Basser Department of Computer Science
Institution: University of Sydney
Year awarded: 1996
MASTER OF COMPUTER SCIENCE
Institution: University of Malaya
Year awarded: 1992
BACHELOR OF ELECTRICAL ENGINEERING
Institution: University of Technology Malaysia
Year awarded: 1986

Publications

Book Chapters

Beliakov, G., Ting, K.M., Murshed, M.M., Rubinov, A., Bertoli, M., 2003, Efficient serial and parallel implementation of the cutting angle method, in High Performance Algorithms and Software for Nonlinear Optimization, eds Gianni Di Pillo and Almerico Murli, Kluwer Academic Publishers, Boston USA, pp. 57-74.

Ting, K.M., 2002, Cost-sensitive classification using decision trees, boosting and MetaCost, in Heuristics and Optimization for Knowledge Discovery, eds Ruhul A. Sarker, Hussein A. Abbass and Charles S. Newton, Idea Group Publishing, Hershey USA, pp. 27-53.

Journal Articles

Ting, K.M., Zhu, L., Wells, J.R., 2013, Local models - the key to boosting stable learners successfully, Computational Intelligence [E], vol E, Elsevier BV, Amsterdam Netherlands, pp. 1-26.

Ting, K.M., Zhou, G.T., Liu, F.T., Tan, S.C., 2013, Mass estimation, Machine Learning [E], vol 90, issue 1, Springer New York LLC, United States, pp. 127-160.

Fu, Z., Lu, G., Ting, K.M., Zhang, D., 2012, Efficient nonlinear classification via low-rank regularised least squares, Neural Computing and Applications [E], vol 21, Springer-Verlag, London UK, pp. 1-17.

Liu, F.T., Ting, K.M., Zhou, Z., 2012, Isolation-based detection, ACM Transactions on Knowledge Discovery from Data [P], vol 6, issue 1, Association for Computing Machinery, Inc., United States, pp. 1-39.

Webb, G., Boughton, J., Zheng, F., Ting, K.M., Salem, H., 2012, Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification, Machine Learning [P], vol 86, issue 2, Springer, Dordrecht Netherlands, pp. 233-272.

Zhou, G.T., Ting, K.M., Liu, F.T., Yin, Y., 2012, Relevance feature mapping for content-based multimedia information retrieval, Pattern Recognition [P], vol 45, issue 4, Pergamon, United Kingdom, pp. 1707-1720.

Tan, S., Ting, K., Teng, S., 2011, A general stochastic clustering method for automatic cluster discovery, Pattern Recognition [P], vol 44, issue 10-11, Pergamon, United Kingdom, pp. 2786-2799.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2011, A survey of audio-based music classification and annotation, IEEE Transactions on Multimedia [P], vol 13, issue 2, The Institute of Electrical and Electronics Engineers, United States, pp. 303-319.

Ting, K., Wells, J., Tan, S., Teng, S., Webb, G.I., 2011, Feature-subspace aggregating: ensembles for stable and unstable learners, Machine Learning [P], vol 82, issue 3, Springer New York LLC, United States, pp. 375-397.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2011, Music classification via the bag-of-features approach, Pattern Recognition Letters [P], vol 32, issue 14, Elsevier BV * North-Holland, Netherlands, pp. 1768-1777.

Washio, T., Suzuki, E., Ting, K., 2010, Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008), Knowledge and Information Systems [P], vol 25, issue 2, Springer - Verlag, London UK, pp. 209-210.

Liu, F.T., Ting, K.M., Yu, Y., Zhou, Z., 2008, Spectrum of Variable-Random trees, The Journal of Artificial Intelligence Research, vol 32, AI Access Foundation Inc, United States, pp. 355-384.

Yang, Y., Webb, G., Korb, K.B., Ting, K.M., 2007, Classifying under computational resource constraints: anytime classification using probabilistic estimators, Machine Learning, vol 69, issue 1, Springer, Netherlands, pp. 35-53.

Yang, Y., Webb, G., Cerquides, J., Korb, K.B., Boughton, J.R., Ting, K.M., 2007, To select or to weigh: A comparative study of linear combination schemes for superparent-one-dependence estimators, IEEE Transactions on Knowledge and Data Engineering, vol 19, issue 12, IEEE Computer Society, New York NY USA, pp. 1652-1665.

Teng, S.W., Ting, K.M., 2006, Ehipasiko: a content-based image indexing and retrieval system, Frontiers in Artificial Intelligence and Applications, vol 138, IOS Press, Netherlands, pp. 436-437.

Webb, G., Ting, K.M., 2005, On the Application of ROC analysis to predict classification performance under varying class distributions, Machine Learning, vol 58, issue 1, Springer, Dordrecht The Netherlands, pp. 25-32.

Ting, K.M., Zheng, Z., 2003, A study of AdaBoost with naive Bayesian classifiers: weakness and improvement, Computational Intelligence: An International Journal, vol 19, issue 2, Blackwell Publishing, Inc., USA, pp. 186-200.

Ting, K.M., 2002, A study on the effect of class distribution using cost-sensitive learning, Lecture Notes in Computer Science: Discovery Science, vol 2534, Springer-Verlag, Berlin Germany, pp. 98-112.

Ting, K.M., 2002, An instance-weighting method to induce cost-sensitive trees, IEEE Transactions on Knowledge and Data Engineering, vol 14, issue 3, Institute of Electrical and Electronics Engineers, Inc., USA, pp. 659-665.

Ting, K.M., 2000, An Empirical Study of MetaCost using Boosting Algorithms, Proceedings of The Eleventh European Conference on Macine Learning. Lecture Notes in Artificial Intelligence (LNAI), vol 1810, Springer-Verlag, Berlin Germany, pp. 413-425.

Conference Proceedings

Wells, J.R., Ting, K.M., Naiwala, C.P., 2012, A non-time series approach to vehicle related time series problems, Proceedings of the Tenth Australasian Data Mining Conference (AusDM'12), 5 December 2012 to 7 December 2012, Australian Computer Society Inc, Sydney NSW Australia, pp. 1-10.

Fu, Z., Lu, G., Ting, K.M., Zhang, D., 2012, Learning sparse kernel classifiers in the primal, Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2012 Proceedings, 7 November 2012 to 9 November 2012, Springer, Berlin Germany, pp. 60-69.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2011, Building sparse support vector machines for multi-instance classification, European Conference on Machine Learning Knowledge Discovery in Databases (ECML PKDD 2011), 5 September 2011 to 9 September 2011, Springer-Verlag, Berlin Germany, pp. 471-486.

Tan, S., Ting, K., Teng, S., 2011, Clustering gene expression data using ant-based heuristics, 2011 IEEE Congress on Evolutionary Computation (CEC), 5th June 2011 to 8th June 2011, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1-8.

Ting, K., Washio, T., Wells, J., Liu, F., 2011, Density estimation based on mass, 11th IEEE International Conference on Data Mining (ICDM 2011), 11 December 2011 to 14 December 2011, IEEE Computer Society, Los Alamitos CA USA, pp. 715-724.

Tan, S., Ting, K., Liu, F., 2011, Fast anomaly detection for streaming data, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), 16 July 2011 to 22 July 2011, AAAI Press, Menlo Park CA USA, pp. 1511-1516.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2011, On low-rank regularized least squares for scalable nonlinear classification, 18th International Conference on Neural Information Processing (ICONIP 2011), 13 November 2011 to 17 November 2011, Springer-Verlag, Berlin Germany, pp. 490-499.

Tan, S.C., Ting, K.M., Teng, S.W., 2011, Simplifying and improving ant-based clustering, International Conference on Computational Science (ICCS 2011), 1 June 2011 to 3 June 2011, Elsevier BV, The Netherlands, pp. 46-55.

Tan, S., Ting, K., Teng, S., 2010, A comparative study of practical stochastic clustering method with traditional methods, Proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence, 7 December 2010 to 10 December 2010, Springer - Verlag, Berlin Germany, pp. 112-121.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2010, Learning naive Bayes classifiers for music classification and retrieval, Proceedings of the 20th International Conference on Pattern Recognition, 23 August 2010 to 26 August 2010, IEEE Computer Society, Piscataway NJ USA, pp. 4589-4592.

Ting, K., Zhou, g., Liu, F., Tan, S., 2010, Mass estimation and its applications, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 25 July 2010 to 28 July 2010, The Association for Computing Machinery, Washington DC USA, pp. 989-998.

Ting, K., Wells, J., 2010, Multi-dimensional mass estimation and mass-based clustering, Proceedings of the 10th IEEE International Conference on Data Mining, 14 December 2010 to 17 December 2010, The Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 511-520.

Liu, F., Ting, K., Zhou, Z., 2010, On detecting clustered anomalies using SCiForest, Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 20 September 2010 to 24 September 2010, Springer - Verlag, Germany, pp. 274-290.

Fu, Z., Lu, G., Ting, K., Zhang, D., 2010, On feature combination for music classification, Proceedings of the Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (SSPR & SPR 2010), 18 August 2010 to 20 August 2010, Springer - Verlag, Germany, pp. 453-462.

Zhou, g., Ting, K., Liu, F., Yin, Y., 2010, Relevance feature mapping for content-based image retrieval, Proceedings of the 16th ACM SIGKDD Workshop on Multimedia Data Mining, 25 July 2010 to 28 July 2010, The Association for Computing Machinery, Washington DC USA, pp. 1-10.

Ting, K.M., Zhu, L., 2009, Boosting support vector machines successfully, Proceedings of the 8th International Workshop on Multiple Classifier Systems, 10 June 2009 to 12 June 2009, Springer - Verlag, Berlin Germany, pp. 509-518.

Ting, K.M., Wells, J.R., Tan, S.C., Teng, S.W., Webb, G., 2009, FaSS: Ensembles for stable learners, Proceedings of the 8th International Workshop on Multipul Classifier Systems (MCS 2009), 10 June 2009 to 12 June 2009, Springer - Verlag, Berlin Germany, pp. 364-374.

Liu, F.T., Ting, K.M., Zhou, Z., 2008, Isolation forest, Proceedingsof the Eighth IEEE International Conference on Data Mining, 15 December 2008 to 19 December 2008, IEEE Computer Society, Los Alamitos CA USA, pp. 413-422.

Tan, S.C., Ting, K.M., Teng, S.W., 2008, Issues of grid-cluster retrievals in swarm-based clustering, Proceedings of the 2008 IEEE World Congress on Computational Intelligence, 1 June 2008 to 6 June 2008, IEEE Press, Piscataway NJ USA, pp. 511-518.

Yu, Y., Zhou, Z., Ting, K.M., 2007, Cocktail ensemble for regression, Proceedings of the Seventh IEEE International Conference on Data Mining, 28 October 2007 to 31 October 2007, IEEE Computer Society, Piscataway NJ USA, pp. 721-726.

Tan, S.C., Ting, K.M., Teng, S.W., 2007, Examining dissimilarity scaling in ant colony approaches to data clustering, Proceedings of the Third Australian Conference on Progress in Artificial Life (ACAL 2007), 4 December 2007 to 6 December 2007, Springer, Germany, pp. 269-280.

Molloy, S.B., Albrecht, D.W., Dowe, D.L., Ting, K.M., 2006, Model-based clustering of sequential data, Proceedings of the 5th Annual Hawaii International Conference on Statistics, Mathematics & Related Fields, 16 January 2006 to 18 January 2006, Hawaii International Conferences, Honolulu USA, pp. 1233-1254.

Tan, S.C., Ting, K.M., Teng, S.W., 2006, Reproducing the results of ant-based clustering without using ants, 2006 IEEE World Congress on Computational Intelligence, 16 July 2006 to 21 July 2006, Omnipress, WI USA, pp. 1760-1767.

Yang, Y., Webb, G., Cerquides, J., Korb, K.B., Boughton, J.R., Ting, K.M., 2006, To select or to weigh: a comparative study of model selection and model weighing for SPODE ensembles, Proceedings of the 17th European Conference on Machine Learning (ECML 2006), 18 September 2006 to 22 September 2006, Springer-Verlag, Germany, pp. 533-544.

Liu, F.T., Ting, K.M., 2006, Variable randomness in decision tree ensembles, Proceedings of the 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2006), 9 April 2006 to 12 April 2006, Springer-Verlag, Germany, pp. 81-90.

Imam, T., Ting, K.M., Kamruzzaman, J., 2006, Z-SVM: an SVM for improved classification of imbalanced data, Proceedings of the 19th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI 2006), 4 December 2006 to 8 December 2006, Springer-Verlag, Germany, pp. 264-273.

Yang, Y., Korb, K.B., Ting, K.M., Webb, G., 2005, Ensemble selection for SuperParent-One-Dependence estimators, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI 2005), 5 December 2005 to 9 December 2005, Springer-Verlag, Berlin Germany, pp. 102-112.

Liu, F.T., Ting, K.M., Fan, W., 2005, Maximizing tree diversity by building complete-random decision trees, Proceedings of the 9th Pacific-Asia Conference in Advances in Knowledge Discovery and Data Mining (PAKDD 2005), 18 May 2005 to 20 May 2005, Springer-Verlag, Berlin Germany, pp. 605-610.

Pang, K.P., Ting, K.M., 2005, SUMSRM: a new statistic for the structural break detection in time series, Proceedings of the Fifth SIAM International Conference on Data Mining, 21 April 2005 to 23 April 2005, Society for Industrial and Applied Mathematics, Philadelphia USA, pp. 392-403.

Pang, K.P., Ting, K.M., 2004, Improving the centered CUSUMS statistic for structural break detection in time series, Proceedings of the 17th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI 2004), 4 December 2004 to 6 December 2004, Springer-Verlag, Berlin Germany, pp. 402-413.

Ting, K.M., 2004, Matching model versus single model: a study of the requirement to match class distribution using decision trees, Proceedings of the 15th European Conference on Machine Learning (ECML 2004), 20 September 2004 to 24 September 2004, Springer-Verlag, Berlin Germany, pp. 429-440.

Wells, J.R., Ting, K.M., Murshed, M.M., Gleb, B., 2003, Analysis of runtime performance using heap data structure for the cutting angle global optimisation algorithm, Proceedings of the 6th International Conference on Computer & Information Technology ICCIT 2003, 19 December 2003 to 21 December 2003, Jahangirnagar University, Dhaka BANGLADESH, pp. 80-85.

Pang, K.P., Ting, K.M., 2003, Improving time series prediction by data selection, Proceedings of the 2003 International Conference on Computational Intelligence for Modelling, Control & Automation - CIMCA '2003, 12 February 2003 to 14 February 2003, DiskTech Pty Ltd, Australia, pp. 803-813.

Ting, K.M., Ying Quek, R.J., 2003, Model stability: a key factor in determining whether an algorithm produces an optimal model from a matching distribution, Proceedings Third IEEE International Conference on Data Mining, 19 November 2003 to 22 November 2003, IEEE Computer Society, Los Alamitos USA, pp. 653-656.

Barnes, M.B., Flitman, A.M., Ting, K.M., 2002, Australian All Ordinaries Index: re-examine the utilities of the explanatory variables using three different measures, Proceedings of the 9th International Conference on Neural Information Processing, 18 November 2002 to 22 November 2002, IEEE Service Centre, Piscataway USA, pp. 2335-2339.

Ting, K.M., 2002, Issues in classifier evaluation using optimal cost curves, Proceedings of the Nineteenth International Conference on Machine Learning, 08 July 2002 to 12 July 2002, Morgan Kaufmann Publishers, San Francisco USA, pp. 642-649.

Beliakov, G., Ting, K.M., Murshed, M., 2001, Efficient serial and parallel implementations of the cutting angle global optimisation technique, Techniques and Applications: Proceedings of the 5th International Conference on Optimization, 15 December 2001 to 17 December 2001, Contemporary Development Company, Hong Kong, pp. 80-87.

Barnes, M.B., Rimmer, R.J., Ting, K.M., 2001, Predicting stock indices n-days ahead: a comparison of techniques using Australian data, Proceedings of the International ICSC-NAISO Congress on Computational Intelligence: Methods and Applications (CIMA-2001), 19 June 2001 to 22 June 2001, ICSC-NAISO Operating Division, Netherland, pp. 295-301.

Ting, K.M., 2000, A Comparative Study of Cost-Sensitive Boosting Algorithms, Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), 29/06/2000 to 02/07/2000, Morgan Kaufmann Publishers, San Francisco CA USA, pp. 983-990.

Other

Webb, G., Yang, Y., Boughton, J.R., Korb, K.B., Ting, K.M., 2005, Classifying under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators, Monash University, Melbourne Vic Australia, pp. 1-24.

Teaching Commitment

Dr Kai Ming Ting is the Chief Examiner for the following unit/s in the Faculty of IT;

  • FIT3002 - Applications of data mining

 Kai Ming teaches the following units in the Faculty of IT;

  • FIT1004 - Data management
  • FIT3002 - Applications of data mining

Activities

Education

Associate Dean (Research Training)
Monash University

Faculty academic committee

Member
Faculty Research Committee
Faculty of Information Technology, Monash University

Faculty executive committee

Member
Senior Leadership Team
Faculty of Information Technology, Monash University

Grants

Title:
Adaptive Anomaly Detection using Isolation Forest.
Investigators:
Ting, K
Funding:
(2009 - 2013). US Air Force Research Laboratory Asian Office of Aerospace Research And Development.
Title:
Automatic music feature extraction, classification and annotation.
Investigators:
Lu, G, Ting, K, Zhang, D
Funding:
(2009 - 2012). Australian Research Council (ARC).
(2009 - 2013). Australian Research Council (ARC).
Title:
Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour.
Investigators:
Albrecht, D, Ting, K, Dowe, D, Kowalczyk, A
Funding:
(2002 - 2006). Australian Research Council (ARC).
(2002 - 2006). Monash University.
(2002 - 2007). Telstra Corporation Ltd.
(2008 - 2012). Australian Research Council (ARC).
(2008 - 2012). Telstra Corporation Ltd.
Title:
Mass Estimation and its Applications.
Investigators:
Ting, K
Funding:
(2010 - 2014). US Air Force Research Laboratory Asian Office of Aerospace Research And Development.
Title:
Resource-bounded adaptive inference of accurate conditional probability estimates from data.
Investigators:
Webb, G, Korb, K, Ting, K
Funding:
(2005 - 2009). Australian Research Council (ARC).
(2005 - 2009). Monash University.
(2007 - 2011). Australian Research Council (ARC).

Postgraduate Research Supervisions

Current Supervision

Program of Study:
(DOCTORATE BY RESEARCH).
Thesis Title:
Developing More Intelligent Information Retrieval Systems.
Supervisors:
Ting, K (Main), Lu, G (Associate).
Program of Study:
(DOCTORATE BY RESEARCH).
Thesis Title:
Mass-based clustering.
Supervisors:
Ting, K (Main).
Program of Study:
(DOCTORATE BY RESEARCH).
Thesis Title:
Mass-based data stream mining.
Supervisors:
Ting, K (Main).
Program of Study:
(MASTER'S BY RESEARCH).
Thesis Title:
Research in Mass based Approaches.
Supervisors:
Ting, K (Main), Webb, G (Associate).

Completed Supervision

Student:
Aryal, S.
Program of Study:
New generative classifiers with mass-based likelihood estimation. (Masters) 2012.
Supervisors:
Ting, K (Main), Chetty, M (Associate).
Student:
Imam, T.
Program of Study:
Frameworks for fast training of support vector machine on large scale data. (PHD) 2008.
Supervisors:
Kamruzzaman, J (Joint), Ting, K (Joint-Co).
Student:
Liu, F.
Program of Study:
Anomaly detection using isolation. (PHD) 2010.
Supervisors:
Ting, K (Main).
Student:
Liu, F.
Program of Study:
The Utility of Randomness in Decision Tree Ensembles. (Masters) 2005.
Supervisors:
Ting, K (Main).
Student:
Pang, K.
Program of Study:
Statistics for structural break detection and their application to forecasting and statistical process control. (PHD) 2005.
Supervisors:
Ting, K (Main), Nath, G (Associate).
Student:
Perumal, N.
Program of Study:
Computing. 2006.
Supervisors:
Ting, K (Main).
Student:
Tan, S.
Program of Study:
Approaches to simplify and improve swarm-based clustering. (PHD) 2008.
Supervisors:
Teng, S (Joint), Ting, K (Joint-Co).
Student:
Teng, S.
Program of Study:
IMAGE INDEXING AND RETRIEVAL BASED ON VECTOR QUANTIZATION. (PHD) 2003.
Supervisors:
Lu, G (Main), Ting, K (Associate).
Student:
Wells, J.
Program of Study:
Ensembles Without Randomisation or Perturbation. (Masters) 2009.
Supervisors:
Ting, K (Main), Teng, S (Associate).
Student:
Wong, S.
Program of Study:
Computing. 2003.
Supervisors:
Ting, K (Main).