Minimum Entropy Clustering and Applications to Gene Expression Analysis

Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First, we propose the minimum entropy (measured on a posteriori probabilities) criterion, which is the conditional entropy of clusters given the observations. Fano's inequality indicates that it could be a good criterion for clustering. We generalize the criterion by replacing Shannon's entropy with Havrda-Charvat's structural -entropy. Interestingly, the minimum entropy criterion based on structural -entropy is equal to the probability error of the nearest neighbor method when = 2. This is another evidence that the proposed criterion is good for clustering. With a nonparametric approach for estimating a posteriori probabilities, an efficient iterative algorithm is then established to minimize the entropy. The experimental results show that the clustering algorithm performs significantly better than...

• Type: Conference
• Year: 2004
• Where: CSB
• Authors: Haifeng Li, Keshu Zhang, Tao Jiang
• Keywords: Bioinformatics, Clustering Algorithms, CSB 2004, Entropy, Minimum Entropy Criterion