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Assessment of Microarray Data Clustering Results Based on a New Geometrical Index for Cluster Validity

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Abstract

A measurement of cluster quality is often needed for DNA microarray data analysis. In this paper, we introduce a new cluster validity index, which measures geometrical features of the data. The essential concept of this index is to evaluate the ratio between the squared total length of the data eigen-axes with respect to the between-cluster separation. We show that this cluster validity index works well for data that contain clusters closely distributed or with different sizes. We verify the method using three simulated data sets, two real world data sets and two microarray data sets. The experiment results show that the proposed index is superior to five other cluster validity indices, including partition coefficients (PC), General silhouette index (GS), Dunn’s index (DI), CH Index and I-Index. Also, we have given a theorem to show for what situations the proposed index works well.

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China

    Benson S. Y. Lam

  2. School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia

    Hong Yan

Authors
  1. Benson S. Y. Lam
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  2. Hong Yan
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Correspondence to Benson S. Y. Lam.

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Lam, B.S.Y., Yan, H. Assessment of Microarray Data Clustering Results Based on a New Geometrical Index for Cluster Validity. Soft Comput 11, 341–348 (2007). https://doi.org/10.1007/s00500-006-0087-1

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  • DOI: https://doi.org/10.1007/s00500-006-0087-1

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