Applying Cluster Ensemble to Adaptive Tree Structured Clustering

URI http://harp.lib.hiroshima-u.ac.jp/hiroshima-cu/metadata/5175
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Title
Applying Cluster Ensemble to Adaptive Tree Structured Clustering
Author
氏名 YAMAGUCHI Takashi
ヨミ ヤマグチ タカシ
別名 市村 匠
氏名 NOGUCHI Yuki
ヨミ ノグチ ユキ
別名
氏名 ICHIMURA Takumi
ヨミ イチムラ タクミ
別名
氏名 MACKIN Kenneth J.
ヨミ マッキン ケネスジェームス
別名
Abstract

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the other hand clustering result stability of ATSC is equally unstable as other divisive hierarchical clustering and partitioned clustering methods.
In this paper, we apply cluster ensemble for each data partition of ATSC in order to improve stability. Cluster ensemble is a framework for improving partitioned clustering stability. As a result of applying cluster ensemble, ATSC yields unique clustering results that could not be yielded by previous hierarchical clustering methods. This is because a different class distances function is used in each division in ATSC.

Description Peer Reviewed
Journal Title
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
Spage
186
Epage
191
Published Date
2009-11
Publisher
IEEE SMC Hiroshima Chapter
ISSN
1883-3977
Language
eng
NIIType
Conference Paper
Text Version
出版社版
Rights
©Copyright by IEEE SMC Hiroshima Chapter. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
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