Density-Sensitive Evolutionary Clustering

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Density-Sensitive Evolutionary Clustering
In this study, we propose a novel evolutionary algorithm-based clustering method, named density-sensitive evolutionary clustering (DSEC). In DSEC, each individual is a sequence of real integer numbers representing the cluster representatives, and each data item is assigned to a cluster representative according to a novel density-sensitive dissimilarity measure which can measure the geodesic distance along the manifold. DSEC searches the optimal cluster representatives from a combinatorial optimization viewpoint using evolutionary algorithm. The experimental results on seven artificial data sets with different manifold structure show that the novel density-sensitive evolutionary clustering algorithm has the ability to identify complex non-convex clusters compared with the K-Means algorithm, a genetic algorithm-based clustering, and a modified K-Means algorithm with the density-sensitive distance metric.
Maoguo Gong, Licheng Jiao, Ling Wang, Liefeng Bo
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Authors Maoguo Gong, Licheng Jiao, Ling Wang, Liefeng Bo
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