Frequent-Pattern based Iterative Projected Clustering

  • Authors:
  • Man Lung Yiu;Nikos Mamoulis

  • Affiliations:
  • -;-

  • Venue:
  • ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
  • Year:
  • 2003

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Abstract

Irrelevant attributes add noise to high dimensional clustersand make traditional clustering techniques inappropriate.Projected clustering algorithms have been proposed to findthe clusters in hidden subspaces. We realize the analogy betweenmining frequent itemsets and discovering the relevantsubspace for a given cluster. We propose a methodology forfinding projected clusters by mining frequent itemsets andpresent heuristics that improve its quality. Our techniquesare evaluated with synthetic and real data; they are scalableand discover projected clusters accurately.