Abstract: Aiming at the shortcomings of clustering algorithms in detecting arbitrary clusters, such as low recognition accuracy, large number of iterations, and poor detection effect, this paper proposes a density clustering algorithm of boundary point division based on local center measure (named DBLCM). Under the limitation of the local center measure, data points are divided into core areas or boundary areas. The points of core region are grouped together to form initial clusters according to the allocation mode of the priority of mutual nearest neighbors, and the points in the boundary region are allocated according to the clusters of the nearest points among its mutual nearest neighbors to obtain the final cluster structure. To verify the algorithm effectiveness, DBLCM is compared with three classic algorithms and three outstanding algorithms newly proposed in recent years on the two-dimensional datasets con
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taining arbitrary shapes and arbitrary densities as well as the multi-dimensional datasets with arbitrary dimensions. In addition, to verify the sensitivity of the parameter k in the DBLCM algorithm, a correlation test is conducted between the k value and the cluster quality on different types of datasets. The experimental results show that the DBLCM algorithm has the advantages such as high recognition accuracy, good ability to detect arbitrary clusters, and no iteration, and it has better comprehensive performance than the six comparison algorithms.

Key words: local central measure, core region, boundary region, mutual nearest neighbor