Clustering Centroid Selection using a K-means and Rapid Density Peak Search Fusion Algorithm
Chenyang Zhang, Jiamei Wang, Xinyun Li, Fei Fu, Weiquan Wang · 2020
In the k-means algorithm, it is difficult to choose the K value and the initial centroids of the clusters. To solve this problem, the K-CFSFDP method, which combines the “clustering by fast search and find of density peaks” (CFSFDP) algorithm and the k-means algorithm, was proposed. In this study, we obtained the optimal value of the hyperparameter dc by using the silhouette coefficient SIL and the error sum of squares SSE to facilitate the selection of dc while testing the cluster centroid determined by the window selection method or the method that first sorts the products of ρi and δi in descending order and then uses the slope change trend on the University of California-Irvine (UCI) dataset. We found that the window selection method was more stable and more effectively enhanced the clustering ability of the proposed k-means and CFSFDP fusion algorithm.