关键词: 聚类, 任意形状的簇, 不同密度的簇, 距离关联性, 动态模型

Abstract: In view of the fact that most of clustering algorithms fail to find arbitrary shaped and different density clusters efficiently, this paper proposes an efficient clustering improved algorithm based on distance-relatedness dynamic model. Firstly, in order to improve the efficiency of clustering, using hierarchical clustering algorithms for the data set to get the initial clusters and remove abnormal clusters. Secondly, in order to obtain arbitrary shaped clusters, taking the centroid of initial clusters as the representative point of all points in it, then running the distance-relatedness dynamic model for clustering, and using the tree structure of hierarchical clustering for pruning. Finally, verifying the effectiveness of the proposed algorithm. The algorithm is tested on the Chameleon dataset, the experimental results show that the algorithm can obtain arbitrary shape and different density clusters, and compared with the same algorithms, the time efficiency is improved significantly.

Key words: clustering, arbitrary shaped clusters, different density clusters, distance-relatedness, dynamic model