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.
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Tan, S.C., Ting, K.M., Teng, S.W. (2007). Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_24
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DOI: https://doi.org/10.1007/978-3-540-76931-6_24
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