Skip to main content
Article thumbnail
Location of Repository

An algorithm for non-distance based clustering in high dimensionalspaces

By Shenghuo Zhu, Tao Li and Mitsuonri Ogihara


Abstract. The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the modelgenerated by the parameters. Experimentalresults on both synthetic data sets and a realdata set show the efficiency and effectiveness of CoFD.

Year: 2002
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.