A novel method for evaluation of clustering results for gene expression data based on entropy theory
Yi Dong · Di-san junyi daxue xuebao · 2004
Objective To establish a systematic framework for the selection of the best clustering algorithm and to provide an entropy evaluation method for clustering analysis of gene expression data. Methods Based on information theory, entropy is used to measure the consistency between the clustering results from six algorithms and the known and validated functional classifications. Results In this study, we applied the entropy method for Lyer's gene expression data. Six entropy curves of clustering algorithms were obtained. Conclusion According to the curve of entropy , both SOM and Fuzzy clustering methods show the highest ability to cluster on these two datasets.