ISSN : 1796-217X
Volume : 4    Issue : 4    Date : June 2009

An Improved Ant Colony Optimization Cluster Algorithm Based on Swarm Intelligence
Weihui Dai, Shouji Liu, and Shuyi Liang
Page(s): 299-306
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This paper proposes an improved ant colony optimization cluster algorithm based on a classics
algorithm - LF algorithm. By the introduction of a new formula and the probability of similarity metric
conversion function, as well as the new formula of distance, this algorithm can deal with the
category data easily. It also introduces a new adjustment process, which adjusts the cluster
generated by the carry process iteratively. We approve that the algorithm can improve the efficiency
and the convergence of the cluster theoretically. Data experiments show that the improved ant
colony algorithm can form more accurate and stability clusters than the K-Modes algorithm,
Information Entropy-Based Cluster Algorithm, and LF Algorithm. Scalability experiments show that
the running time has an obvious linear relationship with the size of data set. Furthermore, we
describe the process and idea of the algorithm usage by a mobile customer classification case and
analyze the cluster results. This algorithm can handle large category dataset more rapidly,
accurately and effectively, and keep the good scalability at the same time.

Index Terms
swarm intelligence, cluster analysis, optimized ant colony algorithm, data mining, category data