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.
This work was supported in part by Grant-in-Aid 18700157 and 18500116 for scientific research from the Ministry of Education, Culture, Sports, Science, and Technology, Japan.
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Iwata, K., Hayashi, A. (2007). Identifying the Underlying Hierarchical Structure of Clusters in Cluster Analysis. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_32
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DOI: https://doi.org/10.1007/978-3-540-74695-9_32
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