Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization

This paper is dealing with the fuzzy clustering method which combines the deterministic annealing (DA) approach with an entropy, especially the Shannon entropy and the Tsallis entropy. By maximizing the Shannon entropy, the fuzzy entropy, or the Tsallis entropy within the framework of the fuzzy c-me...

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Main Author: Makoto Yasuda
Format: Article
Language:English
Published: Hindawi Limited 2011-01-01
Series:Advances in Fuzzy Systems
Online Access:http://dx.doi.org/10.1155/2011/960635
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spelling doaj-aa22eb8e490f4515a7adc10a0fe8114c2020-11-25T00:29:54ZengHindawi LimitedAdvances in Fuzzy Systems1687-71011687-711X2011-01-01201110.1155/2011/960635960635Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy MaximizationMakoto Yasuda0Department of Electrical and Computer Engineering, Gifu National College of Technology, Kamimakuwa 2236-2, Motosu, Gifu 501-0495, JapanThis paper is dealing with the fuzzy clustering method which combines the deterministic annealing (DA) approach with an entropy, especially the Shannon entropy and the Tsallis entropy. By maximizing the Shannon entropy, the fuzzy entropy, or the Tsallis entropy within the framework of the fuzzy c-means (FCM) method, membership functions similar to the statistical mechanical distribution functions are obtained. We examine characteristics of these entropy-based membership functions from the statistical mechanical point of view. After that, both the Shannon- and Tsallis-entropy-based FCMs are formulated as DA clustering using the very fast annealing (VFA) method as a cooling schedule. Experimental results indicate that the Tsallis-entropy-based FCM is stable with very fast deterministic annealing and suitable for this annealing process.http://dx.doi.org/10.1155/2011/960635
collection DOAJ
language English
format Article
sources DOAJ
author Makoto Yasuda
spellingShingle Makoto Yasuda
Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
Advances in Fuzzy Systems
author_facet Makoto Yasuda
author_sort Makoto Yasuda
title Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
title_short Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
title_full Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
title_fullStr Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
title_full_unstemmed Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
title_sort deterministic annealing approach to fuzzy c-means clustering based on entropy maximization
publisher Hindawi Limited
series Advances in Fuzzy Systems
issn 1687-7101
1687-711X
publishDate 2011-01-01
description This paper is dealing with the fuzzy clustering method which combines the deterministic annealing (DA) approach with an entropy, especially the Shannon entropy and the Tsallis entropy. By maximizing the Shannon entropy, the fuzzy entropy, or the Tsallis entropy within the framework of the fuzzy c-means (FCM) method, membership functions similar to the statistical mechanical distribution functions are obtained. We examine characteristics of these entropy-based membership functions from the statistical mechanical point of view. After that, both the Shannon- and Tsallis-entropy-based FCMs are formulated as DA clustering using the very fast annealing (VFA) method as a cooling schedule. Experimental results indicate that the Tsallis-entropy-based FCM is stable with very fast deterministic annealing and suitable for this annealing process.
url http://dx.doi.org/10.1155/2011/960635
work_keys_str_mv AT makotoyasuda deterministicannealingapproachtofuzzycmeansclusteringbasedonentropymaximization
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