Clustering Algorithm Based on Fuzzy C-means and Artificial Fish Swarm

被引：15
|
作者：
Zhu, Weiling [1 ]
Jiang, Jingqing [1 ]
Song, Chuyi [1 ]
Bao, Lanying [1 ]
机构：
[1] Inner Mongolia Univ Nationalities, Coll Math, Tongliao 028000, Peoples R China
来源：
2012 INTERNATIONAL WORKSHOP ON INFORMATION AND ELECTRONICS ENGINEERING | 2012年 / 29卷
关键词：
Artificial fish swarm algorithm; Information entropy; Fuzzy C-means algorithm;
D O I：
10.1016/j.proeng.2012.01.485
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy C-means clustering algorithm. And then the new bionic optimization algorithm---artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology
引用
收藏
页码：3307 / 3311
页数：5
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