A Novel Clustering Algorithm for Asymmetric Dataset

Many clustering methods have been proposed in the area of data mining, but only few of them focused on asymmetric dataset. In this paper, a novel clustering algorithm for asymmetric dataset-PFHC, which is based on FHC[5], is presented. Firstly, dataset is divided into several local regions according to the data density of distribution, where the data density in any local regions is symmetrical. In order to achieve the goal, local epsiv and lambda are used in each local area. In every region, FHC is used to get local clusters. Finally local clusters need to be merged to get the global clusters. As extent of FHC, PFHC runs effective and efficient as experiment shows. Furthermore, PFHC generates better quality clusters than traditional algorithms, and scales up well for large databases, as FHC does.