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A fast method for discovering suitable number of clusters for fuzzy clustering

A fast method for discovering suitable number of clusters for fuzzy clustering One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Intelligent Data Analysis IOS Press

A fast method for discovering suitable number of clusters for fuzzy clustering

Intelligent Data Analysis , Volume 26 (6): 16 – Nov 12, 2022

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References (33)

Publisher
IOS Press
Copyright
Copyright © 2022 © 2022 – IOS Press. All rights reserved.
ISSN
1088-467X
eISSN
1571-4128
DOI
10.3233/ida-200511
Publisher site
See Article on Publisher Site

Abstract

One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness.

Journal

Intelligent Data AnalysisIOS Press

Published: Nov 12, 2022

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