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Symbolic Cluster Analysis for Distribution Valued Dissimilarity
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 Title & Authors
Symbolic Cluster Analysis for Distribution Valued Dissimilarity
Matsui, Yusuke; Minami, Hiroyuki; Misuta, Masahiro;
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We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.
Symbolic Data Analysis;hierarchical clustering;big data;distribution valued data;network latency;
 Cited by
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