Professor Geoffrey McLachlan

Professor

Mathematics
Faculty of Science
g.mclachlan@uq.edu.au
+61 7 336 52150

Overview

Professor Geoffrey McLachlan's research interests are in: data mining, statistical analysis of microarray, gene expression data, finite mixture models and medical statistics.

Professor McLachlan received his PhD from the University of Queensland in 1974 and his DSc from there in 1994. His current research projects in statistics are in the related fields of classification, cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition, and in the field of statistical inference. The focus in the latter field has been on the theory and applications of finite mixture models and on estimation via the EM algorithm.

A common theme of his research in these fields has been statistical computation, with particular attention being given to the computational aspects of the statistical methodology. This computational theme extends to Professor McLachlan's more recent interests in the field of data mining.

He is also actively involved in research in the field of medical statistics and, more recently, in the statistical analysis of microarray gene expression data.

Qualifications

  • Fellow, Australian Mathematical Society
  • GCEd, The University of Queensland
  • Doctor of Science, The University of Queensland
  • PhD, The University of Queensland
  • Bachelor of Science (Honours), The University of Queensland

Publications

View all Publications

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Publications

Book

Book Chapter

  • McLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier.

  • McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing.

  • Lee, Sharon X. and McLachlan, Geoffrey J. (2018). Risk measures based on multivariate skew normal and skew t-mixture models. Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns. (pp. 152-168) edited by Jamie Alcock and Stephen Satchell. Chichester, West Sussex United Kingdom: John Wiley & Sons. doi: 10.1002/9781119288992.ch7

  • McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19

  • Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite Mixture Models in Biostatistics. Handbook of Statistics. (pp. 75-102) Amsterdam, AE Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005

  • Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-56) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Berlin: Springer-Verlag. doi: 10.1007/978-3-319-55723-6

  • Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4

  • McLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2

  • McLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-172) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706

  • McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6

  • McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6

  • McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1

  • McLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. Statistical methods for microarray data analysis: methods and protocols. (pp. 103-119) edited by Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile. New York, NY, United States: Humana Press. doi: 10.1007/978-1-60327-337-4_7

  • McLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. Journeys to data mining: experiences from 15 renowned researchers. (pp. 147-171) edited by Mohamed Medhat Gaber. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-28047-4_12

  • Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 139-172) edited by James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori. Berlin & New York: Springer. doi: 10.1007/978-3-642-21551-3__6

  • McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analysers for the analysis of high-dimensional data. Mixtures: estimation and applications. (pp. 189-212) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119995678.ch9

  • McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. Mixture estimation and applications. (pp. 171-191) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, United Kingdom: John Wiley and Sons.

  • McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the SocietàItaliana di Statistica, Macerata, Italy 12-14 September, 2007. (pp. 3-11) edited by Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre. Berlin; Heidelberg, Germany: Springer - Verlag. doi: 10.1007/978-3-642-03739-9_1

  • McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. Advances in Data Analysis, Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC Helmut-Schmidt-University, Hamburg, July 16–18, 2008. (pp. 33-44) edited by Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-01044-6

  • Ng, Shu-Kay and McLachlan, Geoffrey J. (2010). Expert networks with mixed continuous and categorical feature variables: A location modeling approach.. Machine learning research progress. (pp. 355-368) edited by Hannah Peters and Mia Vogel. New York, U.S.A.: Nova Science.

  • McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation. (pp. 177-184) edited by Hermann Locarek-Junge and Claus Weihs. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-10745-0

  • Flack, L. K. and McLachlan, G. J. (2009). Clustering methods for gene-expression data. Handbook of Research on Systems Biology Applications in Medicine. (pp. 209-220) edited by Andriani Daskalaki. United States: IGI Global. doi: 10.4018/978-1-60566-076-9.ch011

  • McLachlan, G. J. and Ng, S-K. (2009). EM. The Top Ten Algorithms in Data Mining. (pp. 93-115) edited by Wu, X. and Kumar, V.. Florida, United States: Chapman & Hall/CRC.

  • McLachlan, G. J. (2009). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 655-681) edited by Steven D. Brown, Roma Tauler and Beata Walczak. Oxford, U.K.: Elsevier Science. doi: 10.1016/B978-044452701-1.00068-5

  • Le Cao, Kim-Anh and McLachlan, Geoffrey J. (2009). Statistical analysis on microarray data: selection of gene prognosis signatures. Computational biology: issues and applications in oncology. (pp. 55-76) edited by Tuan Pham. New York, United States: Springer. doi: 10.1007/978-1-4419-0811-7_3

  • McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. Bioinformatics, volume 2: Structure, function and applications. (pp. 423-439) edited by J. M. Keith. New Jersey, United States: Humana Press. doi: 10.1007/978-1-60327-429-6_22

  • McLachlan, Geoffrey J., Ng, Angus and Bean, Richard W. (2008). Clustering of microarray data via mixture models. Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics. (pp. 365-383) edited by Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470181218.ch21

  • McLachlan, G J., Chevelu, J. and Zhu, J. (2008). Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. (pp. 364-376) edited by Balakrishnan, N., Pena, E. A. and Silvapulle, M. J.. United States: Institute of Mathematical Statistics. doi: 10.1214/193940307000000284

  • Jones, L., Ng, S., Ambroise, C, Monico, K. A., Khan, N. and McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods of microarray data analysis IV. (pp. 163-173) edited by Jennifer S. Shoemaker and Simon M. Lin. New York, USA: Springer. doi: 10.1007/0-387-23077-7_13

  • Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 137-168) edited by J.E. Gentle, W. Hardle and Y. Mori. Germany: Springer-Verlag.

  • McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. Exploratory Data Analysis in Empirical Research. (pp. 141-148) edited by M. Schwaiger and O. Opitz. Germany: Springer.

Journal Article

Conference Publication

Edited Outputs

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Associate Advisor

Completed Supervision