Mun-Su Kang, Young-Sik Choi, Journal of Internet Computing and Services, Vol. 15, No. 5, pp. 95-106, Oct. 2014
10.7472/jksii.2014.15.5.95, Full Text:  HTML
Keywords: Ant-based clustering, ant-based hierarchical clustering, clustering, swarm intelligence

Abstract

In this paper, we present a novel ant-based hierarchical clustering algorithm, where ants repeatedly hop from one node to another over a weighted directed graph of k-nearest neighborhood obtained from a given dataset. We introduce a notion of node pheromone, which is the summation of amount of pheromone on incoming arcs to a node. The node pheromone can be regarded as a relative density measure in a local region. After a finite number of ants' hopping, we remove nodes with a small amount of node pheromone from the directed graph, and obtain a group of strongly connected components as clusters. We iteratively do this removing process from a low value of threshold to a high value, yielding a hierarchy of clusters. We demonstrate the performance of the proposed algorithm with synthetic and real data sets, comparing with traditional clustering methods. Experimental results show the superiority of the proposed method to the traditional methods.

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