A coarse-grain grid-based subspace clustering method for online multi-dimensional data streams

Jae Woo Lee, Won Suk Lee

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

3 Citations (Scopus)

Abstract

This paper proposes a subspace clustering algorithm which combines grid-based clustering with frequent itemset mining. Given a d-dimensional data stream, the on-going distribution statistics of its data elements in every one-dimensional data space is monitored by a list of fine-grain grid-cells called a sibling list, so that all the one-dimensional clusters are accurately identified. By tracing a set of frequently co-occurred one-dimensionalclusters, it is possible to find a coarse-grain dense rectangular space in a higher dimensional subspace. An ST-tree is introduced to continuously monitor dense rectangular spaces in all the subspaces of the d dimensions. Among the spaces, those ones whose densities are greater than or equal to a user defined minimum support threshold Smin are corresponding to final clusters.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08
Pages1521-1522
Number of pages2
DOIs
Publication statusPublished - 2008
Event17th ACM Conference on Information and Knowledge Management, CIKM'08 - Napa Valley, CA, United States
Duration: 2008 Oct 262008 Oct 30

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other17th ACM Conference on Information and Knowledge Management, CIKM'08
CountryUnited States
CityNapa Valley, CA
Period08/10/2608/10/30

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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