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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

  • Conference Publication: PPEM: privacy-preserving EM learning for mixture models

    Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019) PPEM: privacy-preserving EM learning for mixture models. Concurrency and Computation: Practice and Experience, . doi:10.1002/cpe.5208

  • Journal Article: Finite mixture models

    McLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019) Finite mixture models. Annual Review of Statistics and Its Application, 6 1: . doi:10.1146/annurev-statistics-031017-100325

  • Conference Publication: Positive data kernel density estimation via the LogKDE package for R

    Nguyen, Hien D., Jones, Andrew T. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. In: AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, (269-280). 28 - 30 November 2018. doi:10.1007/978-981-13-6661-1_21

View all Publications

Grants

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Supervision

  • Deep learning approaches to the modelling of neuroimaging and neuroactivation data

    Doctor Philosophy

  • Statistical approaches for automated classification and anomaly detection in Astronomy

    Doctor Philosophy

  • Model-Based Discriminant Analysis of High-Dimensional Data

    (2016) Doctor Philosophy

View all Supervision

Publications

Book

Book Chapter

  • Risk measures based on multivariate skew normal and skew t-mixture models

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

  • Clustering

    McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. In Jonathan M. Keith (Ed.), Bioinformatics Vol. II: Structure, Function, and Applications 2nd ed. (pp. 345-362) New York, NY, United States: Humana Press. doi:10.1007/978-1-4939-6613-4_19

  • Finite Mixture Models in Biostatistics

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

  • On the identification of correlated differential features for supervised classification of high-dimensional data

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

  • Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test

    Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. In Shivakumar Keerthikumar and Suresh Mathivanan (Ed.), Proteome bioinformatics (pp. 109-117) New York, NY, United States: Humana Press. doi:10.1007/978-1-4939-6740-7_9

  • Application of mixture models to large datasets

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

  • Mixture distributions - further developments

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

  • Mixture models for standard p-dimensional Euclidean data

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

  • Computation: Expectation-Maximization Algorithm

    McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. In 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

  • Mixture Models in Statistics

    McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. In 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

  • Multivariate Analysis: Classification and Discrimination

    McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. In 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

  • Clustering of gene expression data via normal mixture models

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

  • An enduring interest in classification: supervised and unsupervised

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

  • The EM algorithm

    Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. In James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori (Ed.), Handbook of Computational Statistics: Concepts and Methods 2nd. rev. and updated ed. ed. (pp. 139-172) Berlin & New York: Springer. doi:10.1007/978-3-642-21551-3__6

  • Mixtures of factor analysers for the analysis of high-dimensional data

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

  • Mixtures of factor analyzers for the analysis of high-dimensional data

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

  • Clustering of high-dimensional and correlated data

    McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. In Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre (Ed.), 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) Berlin; Heidelberg, Germany: Springer - Verlag. doi:10.1007/978-3-642-03739-9_1

  • Clustering of high-dimensional data via finite mixture models

    McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. In Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch (Ed.), 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) Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-01044-6

  • Expert networks with mixed continuous and categorical feature variables: A location modeling approach.

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

  • Use of mixture models in multiple hypothesis testing with applications in bioinformatics

    McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. In Hermann Locarek-Junge and Claus Weihs (Ed.), 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) Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-10745-0

  • Clustering methods for gene-expression data

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

  • EM

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

  • Model-based clustering

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

  • Statistical analysis on microarray data: selection of gene prognosis signatures

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

  • Clustering

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

  • Clustering of microarray data via mixture models

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

  • Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data

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

  • Use of microarray data via model-based classification in the study and prediction of survival from lung cancer

    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. In Jennifer S. Shoemaker and Simon M. Lin (Ed.), Methods of microarray data analysis IV (pp. 163-173) New York, USA: Springer. doi:10.1007/0-387-23077-7_13

  • The EM algorithm

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

  • On clustering by mixture models

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

Journal Article

Conference Publication

  • PPEM: privacy-preserving EM learning for mixture models

    Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019) PPEM: privacy-preserving EM learning for mixture models. Concurrency and Computation: Practice and Experience, . doi:10.1002/cpe.5208

  • Positive data kernel density estimation via the LogKDE package for R

    Nguyen, Hien D., Jones, Andrew T. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. In: AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, (269-280). 28 - 30 November 2018. doi:10.1007/978-981-13-6661-1_21

  • Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

    Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. In: Proceedings of the 2017 IEEE Trustcom/BigDataSE/ICESS. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, (1082-1089). 1 - 4 August 2017. doi:10.1109/Trustcom/BigDataSE/ICESS.2017.356

  • Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization–Minimization Algorithm Approach

    Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization–Minimization Algorithm Approach. In: Proceedings of Future Technologies Conference (FTC) 2017. Future Technologies Conference (FTC) 2017, Vancouver, Canada, (439-446). 29-30 November 2017.

  • On the identification of correlated differential features for supervised classification of high-dimensional data

    Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. In: Francesco Palumbo, Angela Montanari and Maurizio Vichi, 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, (43-57). July 5-8, 2015. doi:10.1007/978-3-319-55723-6_4

  • Privacy distributed three-party learning of Gaussian mixture models

    Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. In: Lynn Batten, Dong Seong Kim, Xuyun Zhang and Gang Li, Proceedings of the 2017 International Conference on Applications and Technologies in Information Security (ATIS). International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, (75-87). 6-7 July 2017. doi:10.1007/978-981-10-5421-1_7

  • A simple parallel EM algorithm for statistical learning via mixture models

    Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. In: Alan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumenstein and Zhiyong Wang, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing, Gold Coast, QLD, Australia, (295-302). 30 November - 2 December,2016. doi:10.1109/DICTA.2016.7796997

  • Finding group structures in "Big Data" in healthcare research using mixture models

    Ng, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. In: Proceedings: 2016 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, (1214-1219). 15-18 December 2016. doi:10.1109/BIBM.2016.7822692

  • On mixture modelling with multivariate skew distributions

    Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. In: Ana Colubi, Angela Blanco and Cristian Gatu, Proceedings of COMPSTAT 2016: 22nd International Conference on Computational Statistics. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, (137-148). 23-26 August 2016.

  • Robust estimation of mixtures of skew-normal distributions

    García-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. In: M. Pratesi and C. Perna, 48th Scientific Meeting of the Italian Statistical Society (SIS2016). Scientific Meeting of the Italian Statistical Society, Salerno, Italy, (). 8-10 November 2016.

  • Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

    Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. In: Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li and Quan Z. Sheng, Proceedings of the 12th International Conference, ADMA 2016. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, (692-699). 12-15 December 2016. doi:10.1007/978-3-319-49586-6_49

  • Application of multiple imputation to incomplete three-way three-mode multi-environment trial data

    Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. In: Abstracts for the XXVIIth International Biometric Conference. International Biometric Conference, Florence (Italy), (). 6-11 July 2014.

  • Asymptotic inference for hidden process regression models

    Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. In: 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, (256-259). 29 June - 2 July 2014. doi:10.1109/SSP.2014.6884624

  • Mixture of regression models with latent variables and sparse coefficient parameters

    Ng, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. In: M. Gilli, G. Gonzaléz-Rodríguez and A. Nieto-Reyes, Proceedings of COMPSTAT 2014, 21st International Conference on Computational Statistics. COMPSTAT 2014, Geneva Switzerland, (). 19- 22 August 2014.

  • A common factor-analytic model for classification

    Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. In: Li, GZ, Kim, S, Hughes, M, McLachlan, G, Sun, H, Hu, X, Ressom, H, Liu, B and Liebman, M, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, (19-24). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732722

  • Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data

    Tian, Ting, McLachlan, Geoff, Dieters, Mark and Basford, Kaye (2013). Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data. In: Abstracts: Biometrics by the Canals. Biometrics by the Canals: The International Biometric Society Australasian Region Conference 2013, Mandura, WA, Australia, (72-72). 1-5 December, 2013.

  • Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk

    Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. In: J. Piantadosi, R. S. Anderssen and J. Boland, Proceedings of the 20th International Congress on Modelling and Simulation. International Congress on Modelling and Simulation, Adelaide, SA, Australia, (1128-1234). 1/12/2013/6/12/2013.

  • On finite mixtures of skew distributions

    McLachlan, Geoffrey J. and Leemaqz, Sharon X. (2013). On finite mixtures of skew distributions. In: Vito M.R. Muggeo, Vincenza Capursi, Giovanni Boscaino and Gianfranco Lovison, Proceedings of the 28th International Workshop on Statistical Modelling. 28th International Workshop on Statistical Modelling, Palermo, Italy, (33-44). 8-12 July 2013.

  • Spatial false discovery rate control for magnetic resonance imaging studies

    Nguyen, Hien D., McLachlan, Geoffrey J., Janke, Andrew L., Cherbuin, Nicolas, Sachdev, Perminder and Anstey, Kaarin J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. In: DeSouza, P, Engelke, U and Rahman, A, Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, (290-297). 26 - 28 November 2013. doi:10.1109/DICTA.2013.6691531

  • Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes

    Ng, Shu-Kay and McLachlan, Geoffrey J. (2013). Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. In: Li, GZ, Kim, S, Hughes, M, McLachlan, G, Sun, H, Hu, X, Ressom, H, Liu, B and Liebman, M, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, (267-272). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732501

  • A comparative study of two matrix factorization methods applied to the classification of gene expression rate

    Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. In: T. Park, L. Chen, L. Wong, S. Tsui, M. Ng and X. Hu, Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, (618-621). 18-21 December 2010.

  • Automated high-dimensional flow cytometric data analysis

    Pyne, Saumyadipta, Hu, Xinli, Wang, Kui, Rossin, Elizabeth, Lin, Tsung-I, Maier, Lisa, Baecher-Allan, Clare, McLachlan, Geoffrey, Tamayo, Pablo, Hafler, David, De Jager, Philip and Mesirov, Jill (2010). Automated high-dimensional flow cytometric data analysis. In: Bonnie Berger, Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010: Proceedings. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, (577-577). 25-28 April 2010. doi:10.1007/978-3-642-12683-3_41

  • Clustering of High-Dimensional Data via Finite Mixture Models

    McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of High-Dimensional Data via Finite Mixture Models. In: Fink, A, Lausen, B, Seidel, W and Ultsch, A, Advances in Data Analysis, Data Handling and Business Intelligence - Proc. of the 32nd Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2008 - Joint Conference with BCS and VOC. 32nd Annual Conference of the German-Classification-Society, Hamburg Germany, (33-+). Jul 16-18, 2008. doi:10.1007/978-3-642-01044-6_3

  • Identifying fibre bundles with regularized k-means clustering applied to grid-based data

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Identifying fibre bundles with regularized k-means clustering applied to grid-based data. In: V. Piuri, Proceedings of the 2010 International Joint Conference on Neural Networks. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, (2281-2288). 18-23 July 2010. doi:10.1109/IJCNN.2010.5596562

  • On relations between genes and metagenes obtained via gradient-based matrix factorization

    Huang, Tian-Hsiang, Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On relations between genes and metagenes obtained via gradient-based matrix factorization. In: Yan Li, Jiajia. Yang, Peng Wen and Jinglong Wu, Proceedings of 2010 IEEE/ICME International Conference on Complex Medical Engineering. 2010 IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, (17-22). 13-15 July 2010. doi:10.1109/ICCME.2010.5558880

  • On the gradient-based algorithm for matrix factorization applied to dimensionality reduction

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On the gradient-based algorithm for matrix factorization applied to dimensionality reduction. In: Ana Fred, Joaquim Filipe and Hugo Gamboa, BIOSTEC 2010: Biomedical Engineering Systems and Technologies. Proceedings of the Third International Joint Conference on Biomedical Engineering Systems and Technologies. BIOINFORMATICS 2010: 1st International Conference on Bioinformatics, Valencia, Spain, (147-152). 20-23 January 2010.

  • Penalized principal component analysis of microarray data

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. In: F. Masulli, L. Peterson and R. Tagliaferri, 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, (82-96). 15-17 October, 2009. doi:10.1007/978-3-642-14571-1_7

  • RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment

    Wojnarski, Marcin, Janusz, Andrzej, Nyugen, Hung Son, Bazan, Jan, Luo, ChuanJiang, Chen, Ze, Hu, Feng, Wang, Guoyin, Guan, Lihe, Luo, Huan, Gao, Juan, Shen, Yuanxia, Nikulin, Vladimir, Huang, Tian-Hsiang, McLachlan, Geoffrey J., Bosnjak, Matko and Gamberger, Dragan (2010). RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment. In: Marcin Szczuka, Marzena Kryszkiewicz, Sheela Ramanna, Richard Jensen and Qinghua Hu, Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing (RSCT 2010). 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010), Warsaw, Poland, (4-19). 28-30 June 2010. doi:10.1007/978-3-642-13529-3_3

  • Classification of imbalanced marketing data with balanced random sets

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. In: Gideon Dror, Marc Boull´e, Isabelle Guyon, Vincent Lemaire and David Vogel, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. AISTATS 2009, Clearwater Beach, FL, United States, (89-100). 16-18 April 2009.

  • Ensemble approach for the classification of imbalanced data

    Nikulin, Vladimir, McLachlan, Geoffrey J. and Ng, Shu Kay (2009). Ensemble approach for the classification of imbalanced data. In: Ann Nicholson, Xiaodong Li, Randy Goebel, Jörg Siekmann and Wolfgang Wahlster, Lecture Notes in Computer Science. AI 2009: Advances in Artificial Intelligence. 22nd Australasian Joint Conference. Proceedings. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, (291-300). 1-4 December 2009. doi:10.1007/978-3-642-10439-8_30

  • Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data

    Wang, Kui, Ng, Shu-Kay and McLachlan, Geoffrey J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. In: Hao Shi, Yanchun Zhang, Murk J. Bottema, Anthony J. Maeder and Brian C. Lovell, Proceedings of Digital Image Computing: Techniques and Applications, 2009. DICTA 2009. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, (526-531). 1-3 December 2009. doi:10.1109/DICTA.2009.88

  • On a general method for matrix factorisation applied to supervised classification

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). On a general method for matrix factorisation applied to supervised classification. In: Jake Chen, Xin Chen, John Ely, Dilek Hakkani-Tr, Jing He, Hui-Huang Hsu, Li Liao, Chunmei Liu, Mihai Pop and Shoba Ranganathan, Proceedings 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., (44-49). 1-4 November 2009. doi:10.1109/BIBMW.2009.5332135

  • Regularised k-means clustering for dimension reduction applied to supervised classification

    Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. In: Francesco Masulli, Leif Peterson and Roberto Tagliaferri, Proceedings of CIBB 2009, Sixth International Meeting on Computational Intelligence for Bioinformatics and Biostatistics. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, (1-10). 15-17 October 2009.

  • Clustering via mixture regression models with random effects

    McLachlan, G. J., Ng, S. K. and Wang, K. (2008). Clustering via mixture regression models with random effects. In: Paula Brito, COMPSTAT : Proceedings in computational statistics. 18th Symposium on Computational Statistics (COMSTAT 2008), Porto, Portugal, (397-407). 24-29 August 2008. doi:10.1007/978-3-7908-2084-3_33

  • Merging algorithm to reduce dimensionality in application to web-mining

    Nikulin, V and McLachlan, GJ (2007). Merging algorithm to reduce dimensionality in application to web-mining. In: Orgun, MA and Thornton, J, AI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence. 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Qld, Australia, (755-761). 2-6 December, 2007. doi:10.1007/978-3-540-76928-6_88

  • Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling

    Lenzenweger, M. F., McLachlan, G. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling. In: Abstracts of the 11th International Congress on Schizophrenia Research. 10th International Congress on Schizophrenia Research, Savannah Ga, (239-240). 02-06 April 2005. doi:10.1093/schbul/sbm004

  • Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites

    McLaren, C. E., Gordeuk, V. R., Chen, W. P., Barton, J. C., Acton, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J., Bean, R. and McLaren, G. D. (2007). Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites. In: 49th Annual Meeting of the American Society of Hematology, Atlanta, GA, U.S.A., (785A-786A). 8 - 11 December 2007.

  • A mixture model with random-effects components for clustering correlated gene-expression profiles

    Ng, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. In: , , (1745-1752). . doi:10.1093/bioinformatics/btl165

  • Clustering replicated microarray data in mixtures of random effects models for varius covariance structures

    Ng, S K, McLachlan, G J, Bean, R W and NG, SW (2006). Clustering replicated microarray data in mixtures of random effects models for varius covariance structures. In: M Boden and T L Bailey, Conferences in Research and Practice in Information Technology. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, (29-33). 4 December 2006.

  • Issues of robustness and high dimensionality in cluster analysis

    Basford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. In: A. Rizzi and M. Vichi, COMPSTAT2006: Proceedings in Computational Statistics. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, (3-15). 28 August - 1 September 2006. doi:10.1007/978-3-7908-1709-6_1

  • Multilevel modelling for inference of genetic regulatory networks

    Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. In: Axel Bender, Complex Systems, Brisbane, Australia, (S390-S390). 11-14 December 2005. doi:10.1117/12.638449

  • Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data

    Ng, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. In: Brian Lovell and Anthony Maeder, Proceedings of WDIC2005. WDIC2005, Griffith University, (139-144). 21 February 2005.

  • Normalized Gaussian Networks with Mixed Feature Data

    Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. In: S. Zhang and R. Jarvis, AI 2005: Advances in Artificial Intelligence. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, (879-882). 5-9 Dec 2005. doi:10.1007/11589990_101

  • Linking gene-expression experiments with survival-time data

    Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. In: A. Biggeri and et al, Proceedings of the 19th International Workship on Statistical Modelling. 19th International Workshop on Statistical Modelling, Florence, (71-75). 4-8 July 2004.

  • On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

    McLachlan, G. J., Chang, S., Mar, J. and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. In: Yi-Ping Phoebe Chen, Proceedings of the Second Asia-Pacific Bioinformatics Conference (APBC2004). Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, (167-171). 18-22 January 2004.

  • On clustering by mixture models

    McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. In: Schwaiger, M and Opitz, O, Exploratory Data Analysis in Empirical Research, Proceedings. 25th Annual Conference of the German-Classification-Society, Munich Germany, (141-148). Mar 14-16, 2001.

  • Robust estimation in Gaussian mixtures using multiresolution Kd -trees

    Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. In: C. Sun, H. Talbot, S. Ourselin and T. Adriaansen, Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, (145-154). 10-12 December 2003.

  • Segmentation of brain MR images with bias field correction

    Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. In: B.C. Lovell, Proceedings of the 2003 APRS Workshop on Digital Image Computing. WDIC 2003, The University of Queensland, Brisbane, (3-8). 7 February 2003.

  • On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches

    Ng, A.S. K. and McLachlan, G. J. (2002). On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches. In: D. Suter and A. Bab-Hadiashar, Digital Image Computing Techniques and Applications. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, (116-121). 21-22 January.

  • Mixture of factor analyzers

    McLachlan, G. J. and Peel, D. (2000). Mixture of factor analyzers. In: P. Langley, Proceedings of the Seventeenth International Conference on Machine Learning. Seventh Intern.Conf. on Machine Learning (ICML - 2000), Stanford University, Calfornia, (599-606). June 29 - July 2, 2000.

  • Multivariate mixture models for classification of anemias

    McLaren, C. E., Cadez, I. V., Smyth, P. and McLachlan, G. J. (2000). Multivariate mixture models for classification of anemias. In: 2000 Proceedings of the Biometrics Section of the American Statistical Association. 2000 Proceedings of the Biometrics Sect. of the Amer.Stat.Ass, Indianapolis, USA, (112-117). August 2000.

  • Computing issues for the EM algorithm in mixture models

    Mclachlan, G. J. and Peel, D. (1999). Computing issues for the EM algorithm in mixture models. In: K. Berk and M. Pourahmadi, Computing Science and Statistics, Proceedings of the 31st Symposium on the Interface. Interface '99, Schaumbury, Illinois, (421-430). June 1999.

  • Extending the two-way mixture model program EMMIX to analyse incomplete data

    Greenway, D. R., Peel, D., Basford, K. E. and McLachlan, G. J. (1999). Extending the two-way mixture model program EMMIX to analyse incomplete data. In: Biometrics 99, Program and Abstracts. Biometrics 99, Univ. of Tas., Hobart, Tas, Aust., (25-26). 12-16 December 1999.

  • Hierarchical models for the screening of iron deficiency anemia

    Cadez, I. V., McLaren, C. E., Smyth, P. and Mclachlan, G. J. (1999). Hierarchical models for the screening of iron deficiency anemia. In: I. Bratko and S. Dzeroski, Proceedings of the 1999 International Conference on Machine Learning. Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, (77-86). June 27-30, 1999.

  • MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models

    McLachlan, GJ and Peel, D (1998). MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models. In: Jain, AK, Venkatesh, S and Lovell, BC, Fourteenth International Conference On Pattern Recognition, Vols 1 and 2. 14th International Conference on Pattern Recognition, Brisbane Australia, (553-557). Aug 16-20, 1998.

  • Robust cluster analysis via mixtures of multivariate t-distributions

    McLachlan G.J. and Peel D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. In: Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. 7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998, , (658-666). August 11, 1998-August 13, 1998.

  • Clustering of magnetic resonance images

    McLachlan, GJ, Ng, SK, Galloway, GJ and Wang, D (1996). Clustering of magnetic resonance images. In: American Statistical Association - 1996 Proceedings of the Statistical Computing Section. Symposium of the Statistical-Computing-Section, at the Annual Meeting of the American-Statistical-Association, Chicago Il, (12-17). Aug 04-08, 1996.

  • The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.

    McLaren, C.E., McLachlan, G.J., Webb, S.J., Jazwinska, E.C., Crawford, D.H.G., Gordeuk, V.R., McLaren, G.D. and Powell, LW (1995). The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.. In: Abstracts of the 37th Annual Meeting of the American Society of Hematology. 37th Annual Meeting of the American Society of Hematology, Seattle, WA, United States, (502-502). 1-5 December 1995.

  • Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests

    McLaren, CE, McLaren, GD, Kambour, EL, McLachlan, GJ, Lukaski, HC, Li, X and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. In: Blood. , , (A116-A116). .

  • Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests

    McLaren, GD, McLaren, CE, Kambour, EL, Lukaski, HC, Xia, L, McLachlan, GJ and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. In: Clinical Research. , , (A405-A405). .

  • Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma

    Holt, JN and McLachlan, GJ (1979). Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma. In: Biometrics. , , (697-697). .

  • Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function

    McLachlan, GJ (1978). Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function. In: Biometrics. , , (172-172). .

Edited Outputs

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Deep learning approaches to the modelling of neuroimaging and neuroactivation data

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Statistical approaches for automated classification and anomaly detection in Astronomy

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • The Application of Advanced Statistical Methods to Hyperspectral Images in Mineral Exploration

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Maximum pseudolikelihood estimation with Markov random fields in the segmentation of brain magnetic resonance images

    Doctor Philosophy — Associate Advisor

    Other advisors:

Completed Supervision

This staff member is a UQ Expert for media in the following fields:

Statistics, Computation - statistics, Image analysis - statistics, Machine learning, Neural networks, Pattern recognition - statistics, Statistical methodology, Data mining, Bioinformatics, Gene expression data, Computer learning

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ResearcherID: A-1491-2008

ORCID: 0000-0002-5921-3145

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