Iterative projected clustering by subspace mining

Man Lung Yiu, Nikos Mamoulis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

59 Citations (Scopus)


Irrolevant attributes add noise to high-dimensional clusters and render traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize the analogy between mining frequent itemsets and discovering dense projected clusters around random points. Based on this, we propose a technique that improves the efficiency of a projected clustering algorithm (DOC). Our method is an optimized adaptation of the frequent pattern tree growth method used for mining frequent itemsets. We propose several techniques that employ the branch and bound paradigm to efficiently discover the projected clusters. An experimental study with synthetic and real data demonstrates that our technique significantly improves on the accuracy and speed of previous techniques. α 2005 IEEE Published by the IEEE Computer Society.
Original languageEnglish
Pages (from-to)176-189
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
Publication statusPublished - 1 Feb 2005
Externally publishedYes


  • Association rules
  • Classification
  • Clustering
  • Database applications
  • Database management

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Iterative projected clustering by subspace mining'. Together they form a unique fingerprint.

Cite this