Abstract
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.
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Gong, M., Jiao, L., Wang, L., Bo, L. (2007). Density-Sensitive Evolutionary Clustering. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_52
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DOI: https://doi.org/10.1007/978-3-540-71701-0_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
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