A Clustering Algorithm for Data Mining Based on Swarm Intelligence

Published 1 January 2007

Peng Jin, Yunlong Zhu, Kunyuan Hu

DOI10.1109/icmlc.2007.4370252

Citations12

TL;DR

A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is proposed, which 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.

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

Computer SciencePhysics and Astronomy

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