A Clustering Algorithm for Data Mining Based on Swarm Intelligence

Author

Jin, Peng ; Zhu, Yun-Long ; Hu, Kun-Yuan

Author_Institution

Chinese Acad. of Sci., Shenyang

Volume

2

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

803

Lastpage

807

Abstract

Clustering analysis is an important function of data mining. Various clustering methods are need for different domains and applications. A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is proposed in this paper. Ant-Cluster algorithm introduces the concept of multi-population of ants with different speed, and adopts fixed moving times method to deal with outliers and locked ant problem. Finally, we experiment on a telecom company´s customer data set with SWARM, agent-based model simulation software, which is integrated in SIMiner, a data mining software system developed by our own studies based on swarm intelligence. The results illuminate that Ant-Cluster algorithm can get clustering results effectively without giving the number of clusters and have better performance than k-means algorithm.

Keywords

data mining; multi-agent systems; particle swarm optimisation; pattern clustering; unsupervised learning; agent-based model simulation software; ant-cluster algorithm; data mining; k-means algorithm; swarm intelligence; Cadaver; Clustering algorithms; Clustering methods; Cybernetics; Data mining; Machine learning; Machine learning algorithms; Particle swarm optimization; Partitioning algorithms; Telecommunications; Clustering algorithm; Data mining; Swarm intelligence;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370252

Filename

4370252