CLICKS

An effective algorithm for mining subspace clusters in categorical datasets

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 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
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsR.L. Grossman, R. Bayardo, K. Bennett, J. Vaidya
Pages736-742
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
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

  • 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. In R. L. Grossman, R. Bayardo, K. Bennett, & J. Vaidya (Eds.), Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 736-742) https://doi.org/10.1145/1081870.1081965

CLICKS : An effective algorithm for mining subspace clusters in categorical datasets. / Zaki, Mohammed J.; Peters, Markus; Assent, Ira; Seidl, Thomas.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / R.L. Grossman; R. Bayardo; K. Bennett; J. Vaidya. 2005. p. 736-742.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zaki, MJ, Peters, M, Assent, I & Seidl, T 2005, CLICKS: An effective algorithm for mining subspace clusters in categorical datasets. in RL Grossman, R Bayardo, K Bennett & J Vaidya (eds), Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 736-742, KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States, 21/8/05. https://doi.org/10.1145/1081870.1081965
Zaki MJ, Peters M, Assent I, Seidl T. CLICKS: An effective algorithm for mining subspace clusters in categorical datasets. In Grossman RL, Bayardo R, Bennett K, Vaidya J, editors, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. p. 736-742 https://doi.org/10.1145/1081870.1081965
Zaki, Mohammed J. ; Peters, Markus ; Assent, Ira ; Seidl, Thomas. / CLICKS : An effective algorithm for mining subspace clusters in categorical datasets. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / R.L. Grossman ; R. Bayardo ; K. Bennett ; J. Vaidya. 2005. pp. 736-742
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