Geoffrey J. McLachlan

Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan: Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. BMC Bioinformatics 13: 300 (2012)

Jangsun Baek, Geoffrey J. McLachlan: Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinformatics 27(9): 1269-1276 (2011)

Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan: Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron. International Journal of Computational Intelligence and Applications 10(1): 1-14 (2011)

Kim-Anh Lê Cao, Emmanuelle Meugnier, Geoffrey J. McLachlan: Integrative mixture of experts to combine clinical factors and gene markers. Bioinformatics 26(9): 1192-1198 (2010)

Jangsun Baek, Geoffrey J. McLachlan, Lloyd K. Flack: Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data. IEEE Trans. Pattern Anal. Mach. Intell. 32(7): 1298-1309 (2010)

Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan: A comparative study of two matrix factorization methods applied to the classification of gene expression data. BIBM 2010: 618-621

Vladimir Nikulin, Geoffrey J. McLachlan: On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction. BIOINFORMATICS 2010: 147-152

Geoffrey J. McLachlan: Assessing the Significance of Groups in High-Dimensional Data. ICDM 2010: 6

Vladimir Nikulin, Geoffrey J. McLachlan: Identifying fiber bundles with regularised к-means clustering applied to the grid-based data. IJCNN 2010: 1-8

Saumyadipta Pyne, Xinli Hu, Kui Wang, Elizabeth Rossin, Tsung I. Lin, Lisa Maier, Clare Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David Hafler, Philip L. De Jager, Jill P. Mesirov: Automated High-Dimensional Flow Cytometric Data Analysis. RECOMB 2010: 577

Vladimir Nikulin, Tian-Hsiang Huang, Shu-Kay Ng, Suren I. Rathnayake, Geoffrey J. McLachlan: A Very Fast Algorithm for Matrix Factorization. CoRR abs/1011.0506 (2010)

Vladimir Nikulin, Geoffrey J. McLachlan: Classification of Imbalanced Marketing Data with Balanced Random Sets. Journal of Machine Learning Research - Proceedings Track 7: 89-100 (2009)

Vladimir Nikulin, Geoffrey J. McLachlan, Shu-Kay Ng: Ensemble Approach for the Classification of Imbalanced Data. Australasian Conference on Artificial Intelligence 2009: 291-300

Vladimir Nikulin, Geoffrey J. McLachlan: Penalized Principal Component Analysis of Microarray Data. CIBB 2009: 82-96

Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan: Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data. DICTA 2009: 526-531

Murray A. Jorgensen, Geoffrey J. McLachlan: Wallace's Approach to Unsupervised Learning: The Snob Program. Comput. J. 51(5): 571-578 (2008)

Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1): 1-37 (2008)

Geoffrey J. McLachlan, Jangsun Baek: Clustering of High-Dimensional Data via Finite Mixture Models. GfKl 2008: 33-44

Shu-Kay Ng, Geoffrey J. McLachlan: Extension of mixture-of-experts networks for binary classification of hierarchical data. Artificial Intelligence in Medicine 41(1): 57-67 (2007)

Jangsun Baek, Young Sook Son, Geoffrey J. McLachlan: Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics 23(4): 458-465 (2007)

Kui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan: Multilevel survival modelling of recurrent urinary tract infections. Computer Methods and Programs in Biomedicine 87(3): 225-229 (2007)

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones: Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. Computational Statistics & Data Analysis 51(11): 5327-5338 (2007)

Kui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan: Two-component Poisson mixture regression modelling of count data with bivariate random effects. Mathematical and Computer Modelling 46(11-12): 1468-1476 (2007)

Vladimir Nikulin, Geoffrey J. McLachlan: Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. Australian Conference on Artificial Intelligence 2007: 755-761

Shu-Kay Ng, Geoffrey J. McLachlan, Andy H. Lee: An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artificial Intelligence in Medicine 36(3): 257-267 (2006)

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones: A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22(13): 1608-1615 (2006)

Shu-Kay Ng, Geoffrey J. McLachlan, Kui Wang, Liat Ben-Tovim Jones, S.-W. Ng: A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22(14): 1745-1752 (2006)

Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu: Mixture Models for Detecting Differentially Expressed Genes in Microarrays. Int. J. Neural Syst. 16(5): 353-362 (2006)

Shu-Kay Ng, Geoffrey J. McLachlan: Normalized Gaussian Networks with Mixed Feature Data. Australian Conference on Artificial Intelligence 2005: 879-882

Richard Bean, Geoffrey J. McLachlan: Cluster Analysis of High-Dimensional Data: A Case Study. IDEAL 2005: 302-310

Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu: Application of Mixture Models to Detect Differentially Expressed Genes. IDEAL 2005: 422-431

Shu-Kay Ng, Geoffrey J. McLachlan: Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition 37(8): 1573-1589 (2004)

Shu-Kay Ng, Geoffrey J. McLachlan: Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. IEEE Transactions on Neural Networks 15(3): 738-749 (2004)

Geoffrey J. McLachlan, Soong Chang, Jess Mar, Christophe Ambroise, Justin Xi Zhu: On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples. APBC 2004: 167-171

Geoffrey J. McLachlan, David Peel, Richard Bean: Modelling high-dimensional data by mixtures of factor analyzers. Computational Statistics & Data Analysis 41(3-4): 379-388 (2003)

J. C. Mar, Geoffrey J. McLachlan: Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. International Journal of Software Engineering and Knowledge Engineering 13(6): 579-592 (2003)

Shu-Kay Ng, Geoffrey J. McLachlan: On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. Statistics and Computing 13(1): 45-55 (2003)

J. C. Mar, Geoffrey J. McLachlan: Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. APBC 2003: 139-144

Shu-Kay Ng, Geoffrey J. McLachlan: Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. DICTA 2003: 145-154

Geoffrey J. McLachlan, Richard Bean, David Peel: A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18(3): 413-422 (2002)

Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren: Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning 47(1): 7-34 (2002)

Geoffrey J. McLachlan, David Peel: Mixtures of Factor Analyzers. ICML 2000: 599-606

Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan: Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86

A. J. Feelders, Soong Chang, Geoffrey J. McLachlan: Mining in the Presence of Selectivity Bias and its Application to Reject Inference. KDD 1998: 199-203

Geoffrey J. McLachlan, David Peel: Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. SSPR/SPR 1998: 658-666

Charles R. O. Lawoko, Geoffrey J. McLachlan: Bias associated with the discriminant analysis approach to the estimation of mixing proportions. Pattern Recognition 22(6): 763-766 (1989)

Charles R. O. Lawoko, Geoffrey J. McLachlan: Further results on discrimination with autocorrelated observations. Pattern Recognition 21(1): 69-72 (1988)

Charles R. O. Lawoko, Geoffrey J. McLachlan: Asymptotic error rates of the W and Z statistics when the training observations are dependent. Pattern Recognition 19(6): 467-471 (1986)

Charles R. O. Lawoko, Geoffrey J. McLachlan: Discrimination with autocorrelated observations. Pattern Recognition 18(2): 145-149 (1985)

Charles R. O. Lawoko, Geoffrey J. McLachlan: Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function. Pattern Recognition 16(1): 119-121 (1983)

S. Ganesalingam, Geoffrey J. McLachlan: Error rate estimation on the basis of posterior probabilities. Pattern Recognition 12(6): 405-413 (1980)

Geoffrey J. McLachlan: A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recognition 9(3): 147-149 (1977)

Geoffrey J. McLachlan: Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognition 8(4): 273-275 (1976)