Frequent-pattern based iterative projected clustering

Man Lung Yiu, Nikos Mamoulis

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

49 Citations (Scopus)

Abstract

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.
Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages689-692
Number of pages4
Publication statusPublished - 1 Dec 2003
Externally publishedYes
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: 19 Nov 200322 Nov 2003

Conference

Conference3rd IEEE International Conference on Data Mining, ICDM '03
Country/TerritoryUnited States
CityMelbourne, FL
Period19/11/0322/11/03

ASJC Scopus subject areas

  • Engineering(all)

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