A genetic clustering algorithm using a message-based similarity measure

被引：20

作者：

Chang, Dongxia ^{[1
,3
]}

Zhao, Yao ^{[1
]}

Zheng, Changwen ^{[2
]}

Zhang, Xianda ^{[3
]}

机构：

[1] Beijing Jiaotong Univ, Inst Informat Sci, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China

[2] Chinese Acad Sci, Inst Software, Natl Key Lab Integrated Informat Syst Technol, Beijing 100080, Peoples R China

[3] Tsinghua Univ, Tsinghua Dept Automat, Beijing 100084, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 02期

基金：

中国博士后科学基金;

关键词：

Clustering; Evolutionary computation; Genetic algorithms; Message passing; K-means algorithm; AUTOMATIC EVOLUTION; FUZZY; NUMBER;

D O I：

10.1016/j.eswa.2011.07.009

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 paper, a genetic clustering algorithm is described that uses a new similarity measure based message passing between data points and the candidate centers described by the chromosome. In the new algorithm, a variable-length real-value chromosome representation and a set of problem-specific evolutionary operators are used. Therefore, the proposed GA with message-based similarity (GAMS) clustering algorithm is able to automatically evolve and find the optimal number of clusters as well as proper clusters of the data set. Effectiveness of GAMS clustering algorithm is demonstrated for both artificial and real-life data set. Experiment results demonstrated that the GAMS clustering algorithm has high performance, effectiveness and flexibility. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：2194 / 2202

页数：9

共 30 条

[1]

Anderson T. W., 1984, An introduction to multivariate statistical analysis, V2nd

[2]

[Anonymous], 1988, Algorithms for Clustering Data

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