Further results on the effect of intraclass correlation among training samples in discriminant analysis. - Geoffrey J. McLachlan (Pattern Recognit., 1976)

A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. - Geoffrey J. McLachlan (Pattern Recognit., 1977)

Error rate estimation on the basis of posterior probabilities. - S. Ganesalingam; Geoffrey J. McLachlan (Pattern Recognit., 1980)

9 The classification and mixture maximum likelihood approaches to cluster analysis. - Geoffrey J. McLachlan (Classification, Pattern Recognition and Reduction of Dimensionality, 1982)

Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function. - Charles R. O. Lawoko; Geoffrey J. McLachlan (Pattern Recognit., 1983)

Discrimination with autocorrelated observations. - Charles R. O. Lawoko; Geoffrey J. McLachlan (Pattern Recognit., 1985)

Asymptotic error rates of the W and Z statistics when the training observations are dependent. - Charles R. O. Lawoko; Geoffrey J. McLachlan (Pattern Recognit., 1986)

Further results on discrimination with autocorrelated observations. - Charles R. O. Lawoko; Geoffrey J. McLachlan (Pattern Recognit., 1988)

Bias associated with the discriminant analysis approach to the estimation of mixing proportions. - Charles R. O. Lawoko; Geoffrey J. McLachlan (Pattern Recognit., 1989)

Maximum likelihood clustering via normal mixture models. - Geoffrey J. McLachlan; David Peel; W. J. Whiten (Signal Process. Image Commun., 1996)

MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models. - Geoffrey J. McLachlan; David Peel (ICPR, 1998)

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

Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. - Geoffrey J. McLachlan; David Peel (SSPR/SPR, 1998)

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

Finite Mixture Models - Geoffrey J. McLachlan; David Peel (Wiley, 2000)

Mixtures of Factor Analyzers. - Geoffrey J. McLachlan; David Peel (ICML, 2000)

Robust mixture modelling using the t distribution. - David Peel; Geoffrey J. McLachlan (Stat. Comput., 2000)

A mixture model-based approach to the clustering of microarray expression data. - Geoffrey J. McLachlan; Richard W. Bean; David Peel (Bioinform., 2002)

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. - Igor V. Cadez; Padhraic Smyth; Geoffrey J. McLachlan; Christine E. McLaren (Mach. Learn., 2002)

Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. - Shu-Kay Ng; Geoffrey J. McLachlan (DICTA, 2003)

Modelling high-dimensional data by mixtures of factor analyzers. - Geoffrey J. McLachlan; David Peel; Richard W. Bean (Comput. Stat. Data Anal., 2003)

Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. - J. C. Mar; Geoffrey J. McLachlan (Int. J. Softw. Eng. Knowl. Eng., 2003)

On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. - Shu-Kay Ng; Geoffrey J. McLachlan (Stat. Comput., 2003)

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

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. - Shu-Kay Ng; Geoffrey J. McLachlan (Pattern Recognit., 2004)

Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. - Shu-Kay Ng; Geoffrey J. McLachlan (IEEE Trans. Neural Networks, 2004)

Normalized Gaussian Networks with Mixed Feature Data. - Shu-Kay Ng; Geoffrey J. McLachlan (Australian Conference on Artificial Intelligence, 2005)

Cluster Analysis of High-Dimensional Data: A Case Study. - Richard Bean 001; Geoffrey J. McLachlan (IDEAL, 2005)

Application of Mixture Models to Detect Differentially Expressed Genes. - Liat Ben-Tovim Jones; Richard Bean 001; Geoffrey J. McLachlan; Justin Xi Zhu (IDEAL, 2005)

An incremental EM-based learning approach for on-line prediction of hospital resource utilization. - Shu-Kay Ng; Geoffrey J. McLachlan; Andy H. Lee (Artif. Intell. Medicine, 2006)

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. - Geoffrey J. McLachlan; Richard W. Bean; Liat Ben-Tovim Jones (Bioinform., 2006)

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

Mixture Models for Detecting Differentially Expressed Genes in Microarrays. - Liat Ben-Tovim Jones; Richard Bean 001; Geoffrey J. McLachlan; Justin Xi Zhu (Int. J. Neural Syst., 2006)

Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. - Vladimir Nikulin; Geoffrey J. McLachlan (Australian Conference on Artificial Intelligence, 2007)

Extension of mixture-of-experts networks for binary classification of hierarchical data. - Shu-Kay Ng; Geoffrey J. McLachlan (Artif. Intell. Medicine, 2007)

Segmentation and intensity estimation of microarray images using a gamma-t mixture model. - Jangsun Baek; Young Sook Son; Geoffrey J. McLachlan (Bioinform., 2007)

Multilevel survival modelling of recurrent urinary tract infections. - Kui Wang; Kelvin K. W. Yau; Andy H. Lee; Geoffrey J. McLachlan (Comput. Methods Programs Biomed., 2007)

Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. - Geoffrey J. McLachlan; Richard W. Bean; Liat Ben-Tovim Jones (Comput. Stat. Data Anal., 2007)

Two-component Poisson mixture regression modelling of count data with bivariate random effects. - Kui Wang; Kelvin K. W. Yau; Andy H. Lee; Geoffrey J. McLachlan (Math. Comput. Model., 2007)

Clustering of High-Dimensional Data via Finite Mixture Models. - Geoffrey J. McLachlan; Jangsun Baek (GfKl, 2008)

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

Top 10 algorithms in data mining. - Xindong Wu 001; Vipin Kumar 001; J. Ross Quinlan; Joydeep Ghosh; Qiang Yang 001; Hiroshi Motoda; Geoffrey J. McLachlan; Angus F. M. Ng; Bing Liu 001; Philip S. Yu; Zhi-Hua Zhou; Michael S. Steinbach; et al. (Knowl. Inf. Syst., 2008)

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

Penalized Principal Component Analysis of Microarray Data. - Vladimir Nikulin; Geoffrey J. McLachlan (CIBB, 2009)

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

Classification of Imbalanced Marketing Data with Balanced Random Sets. - Vladimir Nikulin; Geoffrey J. McLachlan (KDD Cup, 2009)

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

On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction. - Vladimir Nikulin; Geoffrey J. McLachlan (BIOINFORMATICS, 2010)

Assessing the Significance of Groups in High-Dimensional Data. - Geoffrey J. McLachlan (ICDM, 2010)

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

Automated High-Dimensional Flow Cytometric Data Analysis. - 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 (RECOMB, 2010)

Integrative mixture of experts to combine clinical factors and gene markers. - Kim-Anh Lê Cao; Emmanuelle Meugnier; Geoffrey J. McLachlan (Bioinform., 2010)

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

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

Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. - Jangsun Baek; Geoffrey J. McLachlan (Bioinform., 2011)

Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron. - Vladimir Nikulin; Tian-Hsiang Huang; Geoffrey J. McLachlan (Int. J. Comput. Intell. Appl., 2011)

An Enduring Interest in Classification: Supervised and Unsupervised. - Geoffrey J. McLachlan (Journeys to Data Mining, 2012)

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

Top-10 Data Mining Case Studies. - Gabor Melli; Xindong Wu 001; Paul Beinat; Francesco Bonchi; Longbing Cao; Rong Duan; Christos Faloutsos; Rayid Ghani; Brendan Kitts; Bart Goethals; Geoffrey J. McLachlan; Jian Pei 001; et al. (Int. J. Inf. Technol. Decis. Mak., 2012)

Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. - Shu-Kay Ng; Geoffrey J. McLachlan (BIBM, 2013)

A common factor-analytic model for classification. - Mingzhu Sun; Geoffrey J. McLachlan (BIBM, 2013)

Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies. - Hien Duy Nguyen; Andrew L. Janke; Nicolas Cherbuin; Geoffrey J. McLachlan; Perminder S. Sachdev; Kaarin Anstey (DICTA, 2013)

On mixtures of skew normal and skew t-distributions. - Sharon X. Lee; Geoffrey J. McLachlan (Adv. Data Anal. Classif., 2013)

On the classification of microarray gene-expression data. - Kaye E. Basford; Geoffrey J. McLachlan; Suren I. Rathnayake (Briefings Bioinform., 2013)

Model-based clustering and classification with non-normal mixture distributions. - Sharon X. Lee; Geoffrey J. McLachlan (Stat. Methods Appl., 2013)

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". - Sharon X. Lee; Geoffrey J. McLachlan (Stat. Methods Appl., 2013)

Asymptotic inference for hidden process regression models. - Hien Duy Nguyen; Geoffrey J. McLachlan (SSP, 2014)

The 2nd special issue on advances in mixture models. - Dankmar Böhning; Christian Hennig; Geoffrey J. McLachlan; Paul D. McNicholas (Comput. Stat. Data Anal., 2014)

Mixture models for clustering multilevel growth trajectories. - Shu-Kay Ng; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2014)

Finite mixtures of multivariate skew t-distributions: some recent and new results. - Sharon X. Lee; Geoffrey J. McLachlan (Stat. Comput., 2014)

False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields. - Hien Duy Nguyen; Geoffrey J. McLachlan; Nicolas Cherbuin; Andrew L. Janke (IEEE Trans. Medical Imaging, 2014)

On the number of components in a Gaussian mixture model. - Geoffrey J. McLachlan; Suren I. Rathnayake (WIREs Data Mining Knowl. Discov., 2014)

Maximum likelihood estimation of Gaussian mixture models without matrix operations. - Hien Duy Nguyen; Geoffrey J. McLachlan (Adv. Data Anal. Classif., 2015)

Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions. - Sharon X. Lee; Geoffrey J. McLachlan (ADMA, 2016)

Finding group structures in "Big Data" in healthcare research using mixture models. - Shu-Kay Ng; Geoffrey J. McLachlan (BIBM, 2016)

A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models. - Sharon X. Lee; Kaleb L. Leemaqz; Geoffrey J. McLachlan (DICTA, 2016)

Partial identification in the statistical matching problem. - Daniel Ahfock; Saumyadipta Pyne; Sharon X. Lee; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2016)

Laplace mixture of linear experts. - Hien Duy Nguyen; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2016)

Maximum likelihood estimation of triangular and polygonal distributions. - Hien Duy Nguyen; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2016)

Linear mixed models with marginally symmetric nonparametric random effects. - Hien Duy Nguyen; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2016)

Mixtures of spatial spline regressions for clustering and classification. - Hien Duy Nguyen; Geoffrey J. McLachlan; Ian A. Wood (Comput. Stat. Data Anal., 2016)

Extending mixtures of factor models using the restricted multivariate skew-normal distribution. - Tsung-I Lin; Geoffrey J. McLachlan; Sharon X. Lee (J. Multivar. Anal., 2016)

A Universal Approximation Theorem for Mixture-of-Experts Models. - Hien Duy Nguyen; Luke R. Lloyd-Jones; Geoffrey J. McLachlan (Neural Comput., 2016)

Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models. - Sharon X. Lee; Geoffrey J. McLachlan (Stat. Comput., 2016)

A Block Minorization-Maximization Algorithm for Heteroscedastic Regression. - Hien Duy Nguyen; Luke R. Lloyd-Jones; Geoffrey J. McLachlan (IEEE Signal Process. Lett., 2016)

Private Distributed Three-Party Learning of Gaussian Mixture Models. - Kaleb L. Leemaqz; Sharon X. Lee; Geoffrey J. McLachlan (ATIS, 2017)

Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering. - Kaleb L. Leemaqz; Sharon X. Lee; Geoffrey J. McLachlan (TrustCom/BigDataSE/ICESS, 2017)

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach. - Hien D. Nguyen 001; Geoffrey J. McLachlan (CoRR, 2017)

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. - Hien Duy Nguyen; Geoffrey J. McLachlan; Pierre Orban; Pierre Bellec; Andrew L. Janke (Neural Comput., 2017)

Positive Data Kernel Density Estimation via the LogKDE Package for R. - Hien D. Nguyen 001; Andrew T. Jones; Geoffrey J. McLachlan (AusDM, 2018)

A globally convergent algorithm for lasso-penalized mixture of linear regression models. - Luke R. Lloyd-Jones; Hien Duy Nguyen; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2018)

Randomized mixture models for probability density approximation and estimation. - Hien Duy Nguyen; Dianhui Wang; Geoffrey J. McLachlan (Inf. Sci., 2018)

logKDE: log-transformed kernel density estimation. - Andrew T. Jones; Hien Duy Nguyen; Geoffrey J. McLachlan (J. Open Source Softw., 2018)

Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. - Hien Duy Nguyen; Jeremy F. P. Ullmann; Geoffrey J. McLachlan; Venkatakaushik Voleti; Wenze Li; Elizabeth M. C. Hillman; David C. Reutens; Andrew L. Janke (Stat. Anal. Data Min., 2018)

A Block EM Algorithm for Multivariate Skew Normal and Skew $t$ -Mixture Models. - Sharon X. Lee; Kaleb L. Leemaqz; Geoffrey J. McLachlan (IEEE Trans. Neural Networks Learn. Syst., 2018)

PPEM: Privacy-preserving EM learning for mixture models. - Sharon X. Lee; Kaleb L. Leemaqz; Geoffrey J. McLachlan (Concurr. Comput. Pract. Exp., 2019)

Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. - Shu-Kay Ng; Richard Tawiah; Geoffrey J. McLachlan (Pattern Recognit., 2019)

Deep Gaussian mixture models. - Cinzia Viroli; Geoffrey J. McLachlan (Stat. Comput., 2019)

Estimation of Classification Rules from Partially Classified Data. - Geoffrey J. McLachlan; Daniel Ahfock (CoRR, 2020)

An l1-oracle inequality for the Lasso in mixture-of-experts regression models. - TrungTin Nguyen; Hien Duy Nguyen; Faicel Chamroukhi; Geoffrey J. McLachlan (CoRR, 2020)

An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. - Daniel Ahfock; Geoffrey J. McLachlan (Stat. Comput., 2020)

Mini-batch learning of exponential family finite mixture models. - Hien Duy Nguyen; Florence Forbes; Geoffrey J. McLachlan (Stat. Comput., 2020)

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. - Sharon X. Lee; Tsung-I Lin; Geoffrey J. McLachlan (Adv. Data Anal. Classif., 2021)

Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review. - Daniel Ahfock; Geoffrey J. McLachlan (CoRR, 2021)

Harmless label noise and informative soft-labels in supervised classification. - Daniel Ahfock; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2021)

Skew-normal generalized spatial panel data model. - Mohadeseh Alsadat Farzammehr; Mohammad Reza Zadkarami; Geoffrey J. McLachlan (Commun. Stat. Simul. Comput., 2021)

Data fusion using factor analysis and low-rank matrix completion. - Daniel Ahfock; Saumyadipta Pyne; Geoffrey J. McLachlan (Stat. Comput., 2021)

Multi-node Expectation-Maximization algorithm for finite mixture models. - Sharon X. Lee; Geoffrey J. McLachlan; Kaleb L. Leemaqz (Stat. Anal. Data Min., 2021)

Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation. - Ziyang Lyu; Daniel Ahfock; Geoffrey J. McLachlan (CoRR, 2022)

Statistical file-matching of non-Gaussian data: A game theoretic approach. - Daniel Ahfock; Saumyadipta Pyne; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2022)

An overview of skew distributions in model-based clustering. - Sharon X. Lee; Geoffrey J. McLachlan (J. Multivar. Anal., 2022)

A new algorithm for support vector regression with automatic selection of hyperparameters. - You-Gan Wang; Jinran Wu; Zhi-Hua Hu; Geoffrey J. McLachlan (Pattern Recognit., 2023)

Functional mixtures-of-experts. - Faïcel Chamroukhi; Nhat Thien Pham; Van Hà Hoang; Geoffrey J. McLachlan (Stat. Comput., 2024)