关键词: 聚类, 基于势能的快速层次聚类（PHA）, 势能聚类, 自动确定聚类数目

Abstract: Potential-based hierarchical agglomerative clustering (PHA) uses a new similarity metric to get clustering results more efficiently. However, it suffers from the problem how to determine the number of clusters automatically. And it assigns?samples according to distance measure, which ignores the influence of potential. To overcome these shortcomings, this paper proposes a new algorithm that can determine the number of clusters automatically. Firstly, two variables are used to find the clustering centers automatically: the potential of each point and the distance from points to their parent nodes. Then, the distance and the potential are used to assign the remaining points. Finally, the experiments on artificial data sets and real data sets show that the new algorithm not only determines the number of clusters automatically, but also has better clustering results.

Key words: clustering, potential-based hierarchical agglomerative clustering (PHA), potential clustering, automatically determining the number of clustering