A combinational clustering method based on artificial immune system and support vector machine

Authors:
Zhonghua Li;Hong-Zhou Tan
Affiliations:
Department of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, China;Department of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, China
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

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Abstract

Clustering is one branch of unsupervised machine learning theory, which has a wide variety of applications in pattern recognition, image processing, economics, document categorization, web mining, etc. Today, we constantly face how to handle a large number of similar data items, which drives many researchers to contribute themselves to this field. Support vector machine provides a new pathway for clustering, however, it behaves bad in handling massive data. As an emergent theory, artificial immune system can effectively recognize antigens and produce the memory antibodies. This mechanism is constantly used to achieve representative or feature data from raw data. A combinational clustering method is proposed in this paper based on artificial immune system and support vector machine. Experimentation in functionality and performance is done in detail. Finally a more challenging application in elevator industry is conducted. The results strongly indicate that this combinational clustering in this paper is of feasibility and of practice. Indexed Terms:Data clustering, combinational clustering, artificial immune system, support vector machine.