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2017 | ||
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j54 | Hien Duy Nguyen, Geoffrey J. McLachlan, Pierre Orban, Pierre Bellec, Andrew L. Janke: Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. Neural Computation 29(4): 990-1020 (2017) | |

2016 | ||

j53 | Hien Duy Nguyen, Geoffrey J. McLachlan, Ian A. Wood: Mixtures of spatial spline regressions for clustering and classification. Computational Statistics & Data Analysis 93: 76-85 (2016) | |

j52 | Hien Duy Nguyen, Geoffrey J. McLachlan: Laplace mixture of linear experts. Computational Statistics & Data Analysis 93: 177-191 (2016) | |

j51 | Hien Duy Nguyen, Geoffrey J. McLachlan: Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics & Data Analysis 102: 23-36 (2016) | |

j50 | Hien Duy Nguyen, Geoffrey J. McLachlan: Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics & Data Analysis 103: 151-169 (2016) | |

j49 | Daniel Ahfock, Saumyadipta Pyne, Sharon X. Lee, Geoffrey J. McLachlan: Partial identification in the statistical matching problem. Computational Statistics & Data Analysis 104: 79-90 (2016) | |

j48 | Tsung I. Lin, Geoffrey J. McLachlan, Sharon X. Lee: Extending mixtures of factor models using the restricted multivariate skew-normal distribution. J. Multivariate Analysis 143: 398-413 (2016) | |

j47 | Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan: A Universal Approximation Theorem for Mixture-of-Experts Models. Neural Computation 28(12): 2585-2593 (2016) | |

j46 | Sharon X. Lee, Geoffrey J. McLachlan: Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing 26(3): 573-589 (2016) | |

j45 | Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan: A Block Minorization-Maximization Algorithm for Heteroscedastic Regression. IEEE Signal Process. Lett. 23(8): 1131-1135 (2016) | |

c29 | Sharon X. Lee, Geoffrey J. McLachlan: Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions. ADMA 2016: 692-699 | |

c28 | Shu-Kay Ng, Geoffrey J. McLachlan: Finding group structures in "Big Data" in healthcare research using mixture models. BIBM 2016: 1214-1219 | |

c27 | Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan: A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models. DICTA 2016: 1-8 | |

i2 | Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan: A block EM algorithm for multivariate skew normal and skew t-mixture models. CoRR abs/1608.02797 (2016) | |

2015 | ||

j44 | Hien Duy Nguyen, Geoffrey J. McLachlan: Maximum likelihood estimation of Gaussian mixture models without matrix operations. Adv. Data Analysis and Classification 9(4): 371-394 (2015) | |

2014 | ||

j43 | Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas: The 2nd special issue on advances in mixture models. Computational Statistics & Data Analysis 71: 1-2 (2014) | |

j42 | Shu-Kay Ng, Geoffrey J. McLachlan: Mixture models for clustering multilevel growth trajectories. Computational Statistics & Data Analysis 71: 43-51 (2014) | |

j41 | Sharon X. Lee, Geoffrey J. McLachlan: Finite mixtures of multivariate skew t-distributions: some recent and new results. Statistics and Computing 24(2): 181-202 (2014) | |

j40 | Hien Duy Nguyen, Geoffrey J. McLachlan, Nicolas Cherbuin, Andrew L. Janke: False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields. IEEE Trans. Med. Imaging 33(8): 1735-1748 (2014) | |

j39 | Geoffrey J. McLachlan, Suren I. Rathnayake: On the number of components in a Gaussian mixture model. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 4(5): 341-355 (2014) | |

c26 | Hien Duy Nguyen, Geoffrey J. McLachlan: Asymptotic inference for hidden process regression models. SSP 2014: 256-259 | |

2013 | ||

j38 | Sharon X. Lee, Geoffrey J. McLachlan: On mixtures of skew normal and skew t-distributions. Adv. Data Analysis and Classification 7(3): 241-266 (2013) | |

j37 | Kaye E. Basford, Geoffrey J. McLachlan, Suren I. Rathnayake: On the classification of microarray gene-expression data. Briefings in Bioinformatics 14(4): 402-410 (2013) | |

j36 | Sharon X. Lee, Geoffrey J. McLachlan: Model-based clustering and classification with non-normal mixture distributions. Statistical Methods and Applications 22(4): 427-454 (2013) | |

j35 | Sharon X. Lee, Geoffrey J. McLachlan: Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications 22(4): 473-479 (2013) | |

c25 | Mingzhu Sun, Geoffrey J. McLachlan: A common factor-analytic model for classification. BIBM 2013: 19-24 | |

c24 | Shu-Kay Ng, Geoffrey J. McLachlan: Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. BIBM 2013: 267-272 | |

c23 | Hien Duy Nguyen, Andrew L. Janke, Nicolas Cherbuin, Geoffrey J. McLachlan, Perminder S. Sachdev, Kaarin Anstey: Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies. DICTA 2013: 1-8 | |

e1 | Guo-Zheng Li, Sunghoon Kim, Michael Hughes, Geoffrey J. McLachlan, Hongye Sun, Xiaohua Hu, Habtom W. Ressom, Baoyan Liu, Michael N. Liebman: 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, December 18-21, 2013 .IEEE Computer Society 2013, ISBN 978-1-4799-1309-1 | |

2012 | ||

j34 | 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) | |

j33 | Gabor Melli, Xindong Wu, Paul Beinat, Francesco Bonchi, Longbing Cao, Rong Duan, Christos Faloutsos, Rayid Ghani, Brendan Kitts, Bart Goethals, Geoffrey J. McLachlan, Jian Pei, Ashok Srivastava, Osmar R. Zaïane: Top-10 Data Mining Case Studies. International Journal of Information Technology and Decision Making 11(2): 389-400 (2012) | |

2011 | ||

j32 | Jangsun Baek, Geoffrey J. McLachlan: Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinformatics 27(9): 1269-1276 (2011) | |

j31 | 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) | |

2010 | ||

j30 | 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) | |

j29 | 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) | |

c22 | 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 | |

c21 | Vladimir Nikulin, Geoffrey J. McLachlan: On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction. BIOINFORMATICS 2010: 147-152 | |

c20 | Geoffrey J. McLachlan: Assessing the Significance of Groups in High-Dimensional Data. ICDM 2010: 6 | |

c19 | Vladimir Nikulin, Geoffrey J. McLachlan: Identifying fiber bundles with regularised к-means clustering applied to the grid-based data. IJCNN 2010: 1-8 | |

c18 | 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 | |

i1 | 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) | |

2009 | ||

c17 | Vladimir Nikulin, Geoffrey J. McLachlan, Shu-Kay Ng: Ensemble Approach for the Classification of Imbalanced Data. Australasian Conference on Artificial Intelligence 2009: 291-300 | |

c16 | Vladimir Nikulin, Geoffrey J. McLachlan: Penalized Principal Component Analysis of Microarray Data. CIBB 2009: 82-96 | |

c15 | Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan: Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data. DICTA 2009: 526-531 | |

c14 | Vladimir Nikulin, Geoffrey J. McLachlan: Classification of Imbalanced Marketing Data with Balanced Random Sets. KDD Cup 2009: 89-100 | |

2008 | ||

j28 | Murray A. Jorgensen, Geoffrey J. McLachlan: Wallace's Approach to Unsupervised Learning: The Snob Program. Comput. J. 51(5): 571-578 (2008) | |

j27 | 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) | |

c13 | Geoffrey J. McLachlan, Jangsun Baek: Clustering of High-Dimensional Data via Finite Mixture Models. GfKl 2008: 33-44 | |

2007 | ||

j26 | 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) | |

j25 | 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) | |

j24 | 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) | |

j23 | 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) | |

j22 | 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) | |

c12 | Vladimir Nikulin, Geoffrey J. McLachlan: Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. Australian Conference on Artificial Intelligence 2007: 755-761 | |

2006 | ||

j21 | 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) | |

j20 | 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) | |

j19 | 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) | |

j18 | 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) | |

2005 | ||

c11 | Shu-Kay Ng, Geoffrey J. McLachlan: Normalized Gaussian Networks with Mixed Feature Data. Australian Conference on Artificial Intelligence 2005: 879-882 | |

c10 | Richard Bean, Geoffrey J. McLachlan: Cluster Analysis of High-Dimensional Data: A Case Study. IDEAL 2005: 302-310 | |

c9 | 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 | |

2004 | ||

j17 | 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) | |

j16 | Shu-Kay Ng, Geoffrey J. McLachlan: Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. IEEE Trans. Neural Networks 15(3): 738-749 (2004) | |

c8 | 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 | |

2003 | ||

j15 | 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) | |

j14 | 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) | |

j13 | 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) | |

c7 | J. C. Mar, Geoffrey J. McLachlan: Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. APBC 2003: 139-144 | |

c6 | Shu-Kay Ng, Geoffrey J. McLachlan: Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. DICTA 2003: 145-154 | |

2002 | ||

j12 | 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) | |

j11 | 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) | |

2000 | ||

j10 | David Peel, Geoffrey J. McLachlan: Robust mixture modelling using the t distribution. Statistics and Computing 10(4): 339-348 (2000) | |

c5 | Geoffrey J. McLachlan, David Peel: Mixtures of Factor Analyzers. ICML 2000: 599-606 | |

1999 | ||

c4 | Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan: Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86 | |

1998 | ||

c3 | Geoffrey J. McLachlan, David Peel: MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models. ICPR 1998: 553-557 | |

c2 | 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 | |

c1 | Geoffrey J. McLachlan, David Peel: Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. SSPR/SPR 1998: 658-666 | |

1996 | ||

j9 | Geoffrey J. McLachlan, David Peel, W. J. Whiten: Maximum likelihood clustering via normal mixture models. Sig. Proc.: Image Comm. 8(2): 105-111 (1996) | |

1989 | ||

j8 | 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) | |

1988 | ||

j7 | Charles R. O. Lawoko, Geoffrey J. McLachlan: Further results on discrimination with autocorrelated observations. Pattern Recognition 21(1): 69-72 (1988) | |

1986 | ||

j6 | 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) | |

1985 | ||

j5 | Charles R. O. Lawoko, Geoffrey J. McLachlan: Discrimination with autocorrelated observations. Pattern Recognition 18(2): 145-149 (1985) | |

1983 | ||

j4 | 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) | |

1980 | ||

j3 | S. Ganesalingam, Geoffrey J. McLachlan: Error rate estimation on the basis of posterior probabilities. Pattern Recognition 12(6): 405-413 (1980) | |

1977 | ||

j2 | 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) | |

1976 | ||

j1 | Geoffrey J. McLachlan: Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognition 8(4): 273-275 (1976) |

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