A mutual information based approach for evaluating the quality of clustering

S. A. Fattah, Chia Chun Lin, Sun-Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper, a new method for evaluating the quality of clustering of genes is proposed based on mutual information criterion. Instead of using the conventional histogram-based modeling method to assess clustering performance, we derive a normalized mutual information criterion utilizing the Gaussian kernel density estimator. In the computation of the mutual information, we propose to use only cluster-centroids instead of involving all the members, which offers a huge computational savings. The proposed algorithm not only considers the cluster size but also takes into consideration the homogeneity within a cluster. One major advantage of the proposed algorithm is that, it is capable of estimating an appropriate number of clusters. Extensive experimentation has been carried out on some synthetic data as well as the most widely used Yeast cell cycle gene expression data. Under various clustering conditions it is found that the proposed method provides an excellent performance in terms of measuring the quality of cluster and identifying the true number of cluster.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages601-604
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Mutual information
  • clustering
  • gene classification
  • kernel density estimator
  • microarray gene expression data
  • probability density function

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