Clustering by peak merging

Abstract

Clustering by fast search-and-find of density peaks (CFSFDP) is a recently developed density-based clustering method that is being widely used as it can effectively detect isolated high-density regions. However, it often fails to identify true cluster structures from data owing to its intrinsic assumption that a cluster has a unique and high-density center, because a single cluster can contain several peaks. We call this the “multi-peak problem”. To overcome this, we propose a peak merging method for clustering. In the proposed algorithm, a valley and its local density are defined to identify the intersection between two adjoined peaks. These are used to construct directed and connected subgraphs, using which we merge multiple peaks if needed. Unlike CFSFDP and its variants, the proposed method is capable of identifying highly complex shaped clusters with no interpretation of a decision graph. Numerical experiments based on synthetic and real datasets demonstrate that our method outperforms the benchmarking methods.