Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods

被引：8

作者：

Yasuda, Makoto ^{[1
]}

Furuhashi, Takeshi ^{[2
]}

机构：

[1] Gifu Natl Coll Technol, Dept Elect & Comp Engn, Motosu 5010495, Japan

[2] Nagoya Univ, Dept Computat Sci & Engn, Nagoya, Aichi 4648603, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2009年 / E92D卷 / 06期

关键词：

fuzzy c-means clustering; fuzzy entropy; Fermi-Dirac distribution; deterministic annealing; simulated annealing; OPTIMIZATION;

D O I：

10.1587/transinf.E92.D.1232

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1232 / 1239

页数：8

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