Differential Evolution Based Fuzzy Clustering

被引：0

作者：

Ravi, V. ^{[1
]}

Aggarwal, Nupur ^{[1
]}

Chauhan, Nikunj ^{[1
]}

机构：

[1] Inst Dev & Res Banking Technol, Hyderabad 500057, Andhra Pradesh, India

来源：

SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING | 2010年 / 6466卷

关键词：

Differential Evolution; Fuzzy Clustering; Xie-Beni Index; Objective Function; Global Optimization; Evolutionary Algorithm; PARAMETER-ESTIMATION; ALGORITHM; OPTIMIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, two new fuzzy clustering (FC) algorithms based on Differential Evolution (DE) are proposed. Five well-known data sets viz. Iris, Wine, Glass, E. Coli and Olive Oil are used to demonstrate the effectiveness of DEFC-1 and DEFC-2. They are compared with Fuzzy C-Means (FCM) algorithm and Threshold Accepting Based Fuzzy Clustering algorithms proposed by Ravi et al., [1]. Xie-Beni index is used to arrive at the 'optimal' number of clusters. Based on the numerical experiments, we infer that, in terms of least objective function value, these variants can be used as viable alternatives to FCM algorithm.

引用

页码：38 / 45

页数：8

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