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›› 2009, Vol. 26 ›› Issue (4): 539-548.DOI: 10.7523/j.issn.2095-6134.2009.4.016

• Research Articles • Previous Articles     Next Articles

Maximum density clustering algorithm

WANG Jing1,2, XIA Lu-Ning2, JING Ji-Wu2   

  1. 1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China;
    2. State Key Lab of Information Security, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
  • Received:2008-10-08 Revised:2009-01-09 Online:2009-07-15

Abstract:

This paper proposes a new clustering algorithm named maximum density clustering algorithm(MDCA). In MDCA the concept of density is introduced to identify the count of clusters automatically.By selecting the densest object as the threshold, densities of those objects around the densest object are reviewed to decide the partition of basic blocks. Then the basic blocks are merged to form clusters of arbitrary shape. Experiments show that the ability and validity of MDCA in processing unknown datasets are all better than traditional partition-based clustering algorithms.

Key words: data mining, clustering algorithm, densest object, k-means, DBSCAN

CLC Number: