Clustering by the K-Means Algorithm Using a Split and Merge Procedure

Fujiki Morii; Kazuko Kurahashi

doi:10.14864/softscis.2006.0.1767.0

SCIS & ISIS 2006

Session ID : SA-F2-6

DOI https://doi.org/10.14864/softscis.2006.0.1767.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

SA-F2 Clustering (1)

Clustering by the K-Means Algorithm Using a Split and Merge Procedure

Fujiki Morii, *Kazuko Kurahashi

Author information

Keywords: clustering, K-Means algorithm, split and merge procedure

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

To realize a reliable classification of data, a new clustering method by the K-Means algorithm using a split and merge procedure is proposed. After ordinary clustering by the K-Means algorithm, a method determining whether or not each decision region should be split is introduced. After splitting each target decision region into subregions by using the K-Means algorithm again, the other subregions except one region are merged into appropriate adjacent decision regions. The goodness of this procedure is demonstrated under some classification experiments.

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