Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Abstract

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

“…Each color stands for one cluster. We compared our method with five clustering methods: k-means [31], DBSCAN [13], DPC [18], DPSLC [32], and LKSM_DPC [33] by using ARI [34], AMI [35] and FMI [36]. ARI (Adjusted Rand Index) is a development of RI (Rand Index), which reflects the degree of overlap between two clusters.…”

Analisis Cluster Penyakit Malaria Provinsi Papua Menggunakan Metode Single Linkage Dan K-Means

“…Dendogram juga paling sering dibuat sebagai output dari pengelompokan hierarkis. Kegunaan utama dendrogram adalah untuk menentukan cara terbaik dalam mengelompokan suatu objek [10], [11].…”

Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

“…Lin proposed an algorithm [26] that used the radius of neighborhood to automatically select a group of possible density peaks, then used potential density peaks as density peaks, and used CFSFDP to generate preliminary clustering results. Finally, single link clustering was used to reduce the number of clusters.…”