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2004
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Minimum Entropy Clustering and Applications to Gene Expression Analysis

10 years 6 months ago
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...
Haifeng Li, Keshu Zhang, Tao Jiang
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CSB
Authors Haifeng Li, Keshu Zhang, Tao Jiang
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