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

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

Research output: Contribution to conferencePaper

19 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
Pages736-742
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2005
EventKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States
Duration: 21 Aug 200524 Aug 2005

Other

OtherKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityChicago, IL
Period21/8/0524/8/05

Keywords

  • Categorical Data
  • Clustering
  • Data Mining
  • K-partite Graph
  • Maximal Cliques

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Zaki, M. J., Peters, M., Assent, I., & Seidl, T. (2005). CLICKS: An effective algorithm for mining subspace clusters in categorical datasets. 736-742. Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States. https://doi.org/10.1145/1081870.1081965