Special clustering algorithms are attractive for the task of grouping an arbitrary shaped database into several proper classes. Until now, a wide variety of clustering algorithms for this task have been proposed, although the majority of these algorithms are density-based. In this paper, the authors extend the dissimilarity measure to compatible measure and propose a new algorithm (ASCCN) based on the results. ASCCN is an unambiguous partition method that groups objects to compatible nucleoids, and merges these nucleoids into different clusters. The application of cluster grids significantly reduces the computational cost of ASCCN, and experiments show that ASCCN can efficiently and effectively group arbitrary shaped data points into meaningful clusters.
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