Asymmetric Agglomerative Hierarchical Clustering Algorithms and Their Evaluations

Authors:
Akinobu Takeuchi;Takayuki Saito;Hiroshi Yadohisa
Affiliations:
Jissen Women's University, Tokyo, Japan;Tokyo Institute of Technology, Tokyo, Japan;Doshisha University, Kyoto, Japan
Venue:
Journal of Classification
Year:
2007

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Abstract

This paper presents asymmetricagglomerative hierarchical clustering algorithms in an extensive view point. First, we develop a new updating formula for these algorithms, proposing a general framework to incorporate many algorithms. Next we propose measures to evaluate the fit of asymmetric clustering results to data. Then we demonstrate numerical examples with real data, using the new updating formula and the indices of fit. Discussing empirical findings, through the demonstrative examples, we show new insights into the asymmetric clustering.