Abstract
Iterative fuzzy clustering algorithms are sensitive to initialization. Swarm based clustering algorithms are able to do a broader search for the best extrema. A swarm inspired clustering approach which searches in fuzzy cluster centroids space is discussed. An evaluation function based on fuzzy cluster validity was used. A swarm based clustering algorithm can be computationally intensive and a data distributed approach to clustering is shown to be effective. It is shown that the swarm based clustering results in excellent data partitions. Further, it shown that the use of a cluster validity metric as the evaluation function enables the discovery of the number of clusters in the data in an automated way.
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Hall, L.O., Kanade, P.M. (2006). Scalable Swarm Based Fuzzy Clustering. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_3
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DOI: https://doi.org/10.1007/3-540-31314-1_3
Publisher Name: Springer, Berlin, Heidelberg
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