Clicks: An effective algorithm for mining subspace clusters in categorical datasets

Mohammed J. Zaki*, Markus Peters, Ira Assent, Thomas Seidl

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

54 Citations (Scopus)

Abstract

We present a novel algorithm called Clicks, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, Clicks mines subspace clusters. It uses a selective vertical method to guarantee complete search. Clicks outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. These results are demonstrated in a comprehensive performance study on real and synthetic datasets.

Original languageEnglish
JournalData and Knowledge Engineering
Volume60
Issue1
Pages (from-to)51-70
Number of pages20
ISSN0169-023X
DOIs
Publication statusPublished - Jan 2007
Externally publishedYes

Keywords

  • Categorical data
  • Clustering
  • k-Partite graph
  • Maximal cliques

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