Examining dissimilarity scaling in ant colony approaches to data clustering

Authors:
Swee Chuan Tan;Kai Ming Ting;Shyh Wei Teng
Affiliations:
Gippsland School of Information Technology, Monash University, Churchill, Victoria, Australia;Gippsland School of Information Technology, Monash University, Churchill, Victoria, Australia;Gippsland School of Information Technology, Monash University, Churchill, Victoria, Australia
Venue:
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Year:
2007

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Abstract

In this paper, we provide the reasons why the dissimilarity-scaling parameter (α) in the neighbourhood function of ant-based clustering is critical for detecting the correct number of clusters in data sources. We then examine a recently proposed method named ATTA; we show that there is no need to use a population of α-adaptive ants to reproduce ATTA's results. We devise a method to estimate a fixed (i.e, non-adaptive) single value of α for each dataset. We also introduce a simplified version of ATTA, called SATTA. The reason for introducing SATTA is two-fold: first, to test our proposed α-estimation method; and, second, to simulate ant-based clustering from a purely stochastic perspective. SATTA omits the ant colony but reuses important ant heuristics. Experimental results show that SATTA generally performs better than ATTA on clusters with different densities and clusters that are elongated. Finally, we show that the results can be further improved using a majority voting scheme.