Sparse learning based on clustering by fast search and find of density peaks

Clustering by fast search and find of density peaks (CFSFDP) is a novel clustering algorithm proposed in recent years. The algorithm has the advantages of low computational complexity and high accuracy. However, the truncation distance d c needs to be determined according to user experience. Aiming to overcome these drawbacks, this paper proposes a new algorithm named Sparse learning based on clustering by fast search and find of density peaks (SL-CFSFDP). Compared to CFSFDP, the proposed algorithm can obtain d c automatically, and it uses sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. SL-CFSFDP combines the local density and the distance δ i to automatically determine cluster centers, after which the remaining data points are assigned to clusters according to the local density and distance δ i . Extensive experimental results on both synthetic and benchmark datasets show that SL-CFSFDP is superior to DBSCAN and CFSFDP.