Journal of Biomedical Informatics
Performance of data resampling methods for robust class discovery based on clustering
Intelligent Data Analysis
Feature-guided clustering of multi-dimensional flow cytometry datasets
Journal of Biomedical Informatics
A novel pattern based clustering methodology for time-series microarray data
International Journal of Computer Mathematics - Bioinformatics
A comprehensive validity index for clustering
Intelligent Data Analysis
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Quality indices for (practical) clustering evaluation
Intelligent Data Analysis
Co-expression gene discovery from microarray for integrative systems biology
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Hi-index | 3.84 |
Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis. Results: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters. Availability: Please contact the first author.