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NSS-AKmeans: An Agglomerative fuzzy K-means clustering method with automatic selection of cluster number

  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present a new Neighbor Sharing Selection based Agglomerative fuzzy K-means (NSS-AKmeans) algorithm for learning optimal number of clusters and generating better clustering results. The NSS-AKmeans can identify high density areas and determine initial cluster centers from these areas with a neighbor sharing selection method. To select initial cluster centers, we propose an agglomeration energy (AE) factor for representing global density relationship of objects, and a Neighbors Sharing Factor (NSF) for estimating local neighbor sharing relationship of objects. Then we use the Agglomerative Fuzzy k-means clustering algorithm to further merge these initial centers to obtain the preferred number of clusters and generate better clustering results. Experimental results on various data sets have shown that the NSS-AKmeans was very effective in automatically identifying the true cluster number as well as producing accurate clustering results.

Original languageEnglish
Title of host publicationProceedings - 2nd IEEE International Conference on Advanced Computer Control, ICACC 2010
Pages32-38
Number of pages7
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Advanced Computer Control, ICACC 2010 -
Duration: 27 Mar 201029 Mar 2010

Publication series

NameProceedings - 2nd IEEE International Conference on Advanced Computer Control, ICACC 2010
Volume2

Conference

Conference2010 IEEE International Conference on Advanced Computer Control, ICACC 2010
Period27/03/1029/03/10

Keywords

  • Agglomeration energy
  • Initial cluster centers
  • Neighbor sharing selection
  • Neighbors sharing factor
  • Number of clusters

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