K-means Algorithm Based on Improved Density Peak Algorithm

Abstract

Abstract：The initial clustering centers and the number Of clusters need to be selected manually in traditional K—means al— gorithm，SO the result of clustering is unstable and easy to fall into local optimal solution．To deal with this problem，this paper proposes a K—means algorithm based on the improved algorithm of density peak(DPC)．The proposed algorithm firstly uses the improved DPC algorithm to select the initial clustering center，SO as to make up for the flaw that the random selection of initial elustering center of[k-means]algorithm leads to the easily trapped local optimal solution，and then uses the K-means algorithm to itcrate and introduce the entropy method to calculate the distance to optimize clustering．The result of experiment on the UCI dataset shows that the proposed algorithm can obtain relatively better initial clustering centers and relatively more stable clustering results，with a faster convergence，thus proving the feasibility of the algorithm．

Tasks

Reproductions