A genetic clustering algorithm using a message-based similarity measure

Authors:
Dongxia Chang;Yao Zhao;Changwen Zheng;Xianda Zhang
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China and Tsinghua Department of Autom ...;Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;National Key Lab of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing 100080, China;Tsinghua Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.