CoFD: An algorithm for non-distance based clustering in high dimensional spaces

Shenghuo Zhu, Tao Li, Mitsuonri Ogihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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. Experimental results on both synthetic data sets and a realdata set show the efficiency and effectiveness of CoFD.

Original languageEnglish (US)
Title of host publicationData Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings
EditorsYahiko Kambayashi, Werner Winiwarter, Masatoshi Arikawa
PublisherSpringer Verlag
Pages52-62
Number of pages11
ISBN (Print)3540441239, 9783540441236
DOIs
StatePublished - 2002
Event4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002 - Aix-en-Provence, France
Duration: Sep 4 2002Sep 6 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2454 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002
CountryFrance
CityAix-en-Provence
Period9/4/029/6/02

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'CoFD: An algorithm for non-distance based clustering in high dimensional spaces'. Together they form a unique fingerprint.

  • Cite this

    Zhu, S., Li, T., & Ogihara, M. (2002). CoFD: An algorithm for non-distance based clustering in high dimensional spaces. In Y. Kambayashi, W. Winiwarter, & M. Arikawa (Eds.), Data Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings (pp. 52-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2454 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-46145-0_6