Abstract
For applications of clustering algorithms, a key technique is to handle complicatedly distributed clusters effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper. Experimental results show that the algorithm can handle clusters of arbitrary shapes, sizes and densities. At the same time, the algorithm can evidently reduce time and space complexity as compared with other density-based algorithms.
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