Cluster ensemble in adaptive tree structured clustering

Authors:
Takashi Yamaguchi;Yuki Noguchi;Kenneth J. Mackin;Takumi Ichimura
Affiliations:
Department of Information Systems, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan.;Department of Information Systems, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan.;Department of Information Systems, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan.;Faculty of Management and Information Systems, Prefectural University of Hiroshima, 1-1-71 Ujina-Higashi, Minami-ku, Hiroshima 734-8558, Japan
Venue:
International Journal of Knowledge Engineering and Soft Data Paradigms
Year:
2011

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Abstract

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into two subsets using self-organising feature map (SOM). In each partition, after the data set is quantised by SOM, the quantised data is divided using agglomerative hierarchical clustering. ATSC can divide the data sets regardless of data size in feasible time. On the other hand the number of cluster and the members of each cluster are not universal in each run. This non-universality is fundamental problem in the other divisive hierarchical clustering and partitioned clustering. In this paper, we apply cluster ensemble to each data partition of ATSC in order to improve universality. Cluster ensemble is a framework by using multiple learners for improving universality. From the computer simulation, we showed that the proposed method is effective for improving universality. Moreover, the accuracy was improved by solving the non-universality of each partition.