关键词: 聚类分析, 任意簇, 密度引力, 局部密度

Abstract:

Existing clustering algorithms often fail in dealing with datasets with various clusters. By regarding each data point as a particle, a new robust density attracted clustering algorithm is proposed to better detect the cluster. First, the local density of a point is obtained according to the dense degree of its neighbors. Then, each point is        assigned to its mutual nearest neighbor with greater density to form initial clusters. Next, the initial clusters including the same points are merged together to form the final clusters. By comparing the new algorithm with the three classical and the three state-of-the-art methods on six various datasets respectively, the results show that the new proposed    algorithm shows the best performance, and can be used to find various clusters in datasets with different dimensions.

Key words: clustering, various clusters, density attraction, local density