Some computational issues in cluster analysis with no a priori metric
Computational Statistics & Data Analysis
A mixture model approach for the analysis of microarray gene expression data
Computational Statistics & Data Analysis
Robust mixture modelling using the t distribution
Statistics and Computing
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Performance of data resampling methods for robust class discovery based on clustering
Intelligent Data Analysis
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Gaussian mixture learning via robust competitive agglomeration
Pattern Recognition Letters
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Test for homogeneity in gamma mixture models using likelihood ratio
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Hi-index | 0.00 |
We consider the problem or assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature.