Identifying the underlying hierarchical structure of clusters in cluster analysis

Authors:
Kazunori Iwata;Akira Hayashi
Affiliations:
Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan;Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

In this paper, we examine analysis of clusters of labeled samples to identify their underlying hierarchical structure. The key in this identification is to select a suitable measure of dissimilarity among clusters characterized by subpopulations of the samples. Accordingly, we introduce a dissimilarity measure suitable for measuring a hierarchical structure of subpopulations that fit the mixture model. Glass identification is used as a practical problem for hierarchical cluster analysis, in the experiments in this paper. In the experimental results, we exhibit the effectiveness of the introduced measure, compared to several others.