PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets Recently many researchers exert their effort on clustering as a primary data mining method for knowledge discovery, but only few of them have focused on uneven dataset. In the last research, we proposed an efficient hierarchical algorithm based on fuzzy graph connectedness-FHC-to discover clusters with arbitrary shapes. In this paper, we present a novel clustering algorithm for uneven dataset-PFHC-which is an extended version based on FHC. In PFHC, dataset is divided into several local spaces firstly according to the data density of distribution, where the data density in any local space is nearly uniform. In order to achieve the goal, local $epsilon $ and $lambda $ are used in each local domain to acquire local clustering result by FHC. Then boundary between local areas needs being taken into consideration for combination. Finally local clusters need to be merged to obtain global clusters. As an extension of FHC, PFHC can deal with uneven datasets more effectively and efficiently, and generate better quality clusters than other methods as experiment shows. Furthermore, PFHC is found to be able to process incremental data as well in this work.