Improved CFDP Algorithms Based on Shared Nearest Neighbors and Transitive Closure

摘要

A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which there exist density fluctuations within a cluster or between two nearby clusters. In this study, two improved algorithms named CFDP-ED-TSNN1 and CFDP-ED-TSNN2 are presented, which adopt different ways to utilize the dissimilarity. Here, the dissimilarity is based on shared nearest neighbors and transitive closure. The experimental results on both several artificial datasets and a real-world dataset show that the improved algorithms are competitive.