The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article explique comment appliquer les méthodes de recuit déterministe (DA) et de recuit simulé (SA) au clustering c-means flou basé sur l'entropie floue. En régularisant la méthode des c-moyennes floues avec une entropie floue, on obtient une fonction d'appartenance similaire à la fonction de distribution de Fermi-Dirac, bien connue en mécanique statistique, et, tout en optimisant ses paramètres par SA, le minimum de l'énergie libre de Helmholtz pour Le clustering c-means flou est recherché par DA. Des expériences numériques sont effectuées et les résultats obtenus indiquent que cet algorithme combinatoire de SA et DA peut représenter diverses formes de cluster et diviser les données de manière plus correcte et plus stable que l'algorithme standard de DA unique.
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Makoto YASUDA, Takeshi FURUHASHI, "Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 6, pp. 1232-1239, June 2009, doi: 10.1587/transinf.E92.D.1232.
Abstract: This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of the Helmholtz free energy for fuzzy c-means clustering is searched by DA. Numerical experiments are performed and the obtained results indicate that this combinatorial algorithm of SA and DA can represent various cluster shapes and divide data more properly and stably than the standard single DA algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1232/_p
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@ARTICLE{e92-d_6_1232,
author={Makoto YASUDA, Takeshi FURUHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods},
year={2009},
volume={E92-D},
number={6},
pages={1232-1239},
abstract={This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of the Helmholtz free energy for fuzzy c-means clustering is searched by DA. Numerical experiments are performed and the obtained results indicate that this combinatorial algorithm of SA and DA can represent various cluster shapes and divide data more properly and stably than the standard single DA algorithm.},
keywords={},
doi={10.1587/transinf.E92.D.1232},
ISSN={1745-1361},
month={June},}
Copier
TY - JOUR
TI - Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods
T2 - IEICE TRANSACTIONS on Information
SP - 1232
EP - 1239
AU - Makoto YASUDA
AU - Takeshi FURUHASHI
PY - 2009
DO - 10.1587/transinf.E92.D.1232
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2009
AB - This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of the Helmholtz free energy for fuzzy c-means clustering is searched by DA. Numerical experiments are performed and the obtained results indicate that this combinatorial algorithm of SA and DA can represent various cluster shapes and divide data more properly and stably than the standard single DA algorithm.
ER -