An insensitivity Fuzzy C-means clustering algorithm based on penalty factor

被引：6

作者：

Chen, Jiashun ^{[1
,2
]}

Pi, Dechang ^{[2
]}

Liu, Zhipeng ^{[2
,3
]}

机构：

[1] Huaihai Institute of Technology, College of Computer Engineering, Lianyungang

[2] Nanjing University of Aeronautics and Astronautics, College of Information Science and Technology, Nanjing

[3] Nanjing University of Posts and Communications, College of Computer Science and Technology, Nanjing

来源：

Journal of Software | 2013年 / 8卷 / 09期

关键词：

Constraint; IFCM; Membership; Noisy data;

D O I：

10.4304/jsw.8.9.2379-2384

中图分类号：

学科分类号：

摘要：

This paper analyzes sensitivity of Fuzzy C-means to noisy which generates unreasonable clustering results. We also find that Fuzzy C-means possess monotonicity, which may generate meaningless clustering results. Aiming at these weak points, we present an improved Fuzzy C-means named IFCM (Improved Fuzzy C-means). Firstly, we research the reason of sensitivity and find that constraint leads to sensitivity of algorithm, we propose abolish constraint; secondly, we replace membership with typicality for acquiring more reasonable clustering results; finally, we add penalty factor to objective function to avoid monotonicity and coincident clustering results. On the basis of these, we improve objective function and provide step of algorithm. Experiments on various datasets show that new algorithm recognizes noisy data effectively and makes cluster effect improve furthermore. © 2013 ACADEMY PUBLISHER.

引用

页码：2379 / 2384

页数：5

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