Computer Science ›› 2018, Vol. 45 ›› Issue (11): 244-248.doi: 10.11896/j.issn.1002-137X.2018.11.038

• Artificial Intelligence • Previous Articles     Next Articles

K-CFSFDP Clustering Algorithm Based on Kernel Density Estimation

DONG Xiao-jun, CHENG Chun-ling   

  1. ( College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
  • Received:2017-10-27 Published:2019-02-25

Abstract: The CFSFDP (Clustering by Fast Search and Find of Density Peaks) is a new density-based clustering algorithm,it can identify the cluster centers effectively by finding the density peaks,and it has the advantages of fast clustering speed and simple realization.The accuracy of CFSFDP algorithm depends on the density estimation in the dataset and cut off distance (dc) of artificial selection.Therefore,an improved K-CFSFDP algorithm based on kernel density estimation was presented.The algorithm uses non parametric kernel density to analyze distribution of data points and selects the dc adaptively to search and find the peak density of data points,with the peak point data as the initial cluster centers.The simulated results on 4 typical datasets show that the K-CFSFDP algorithm has better performance in accuracy and better robustness than CFSFDP,K-means and DBSCAN algorithm.

Key words: Cluster center, Clustering, Density peak, Kernel density estimation

CLC Number: 

  • TP311
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