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Identifying the Underlying Hierarchical Structure of Clusters in Cluster Analysis.
- Source :
- Artificial Neural Networks - ICANN 2007; 2007, p311-320, 10p
- Publication Year :
- 2007
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540746935
- Database :
- Complementary Index
- Journal :
- Artificial Neural Networks - ICANN 2007
- Publication Type :
- Book
- Accession number :
- 33107109
- Full Text :
- https://doi.org/10.1007/978-3-540-74695-9_32