Robust clustering in arbitrarily oriented subspaces

Elke Achtert*, Christian Böhm, Jörn David, Peer Kröger, Arthur Zimek

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper, we propose an efficient and effective method to find arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set of possible arbitrarily oriented subspaces. The objective of a clustering algorithm based on this principle is to find those among all the possible subspaces, that accommodate many database objects. In contrast to existing approaches, our method can find subspace clusters of different dimensionality even if they are sparse or are intersected by other clusters within a noisy environment. A broad experimental evaluation demonstrates the robustness, efficiency and effectivity of our method.

Original languageEnglish
Title of host publicationSociety for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130
EditorsChid Apte, Haesun Park, Ke Wang, Mohammad J. Zaki
Volume2
PublisherSociety for Industrial and Applied Mathematics
Publication dateOct 2008
Pages763-774
ISBN (Print)978-0-89871-654-2
ISBN (Electronic)978-1-61197-278-8
DOIs
Publication statusPublished - Oct 2008
Externally publishedYes
Event8th SIAM International Conference on Data Mining 2008 - Atlanta, United States
Duration: 24. Apr 200826. Apr 2008

Conference

Conference8th SIAM International Conference on Data Mining 2008
CountryUnited States
CityAtlanta
Period24/04/200826/04/2008
SeriesS I A M Proceedings in Applied Mathematics
Volume130

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Clustering algorithms
Object-oriented databases

Cite this

Achtert, E., Böhm, C., David, J., Kröger, P., & Zimek, A. (2008). Robust clustering in arbitrarily oriented subspaces. In C. Apte, H. Park, K. Wang, & M. J. Zaki (Eds.), Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130 (Vol. 2, pp. 763-774). Society for Industrial and Applied Mathematics. S I A M Proceedings in Applied Mathematics, Vol.. 130 https://doi.org/10.1137/1.9781611972788.69
Achtert, Elke ; Böhm, Christian ; David, Jörn ; Kröger, Peer ; Zimek, Arthur. / Robust clustering in arbitrarily oriented subspaces. Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. editor / Chid Apte ; Haesun Park ; Ke Wang ; Mohammad J. Zaki. Vol. 2 Society for Industrial and Applied Mathematics, 2008. pp. 763-774 (S I A M Proceedings in Applied Mathematics, Vol. 130).
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Achtert, E, Böhm, C, David, J, Kröger, P & Zimek, A 2008, Robust clustering in arbitrarily oriented subspaces. in C Apte, H Park, K Wang & MJ Zaki (eds), Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. vol. 2, Society for Industrial and Applied Mathematics, S I A M Proceedings in Applied Mathematics, vol. 130, pp. 763-774, 8th SIAM International Conference on Data Mining 2008, Atlanta, United States, 24/04/2008. https://doi.org/10.1137/1.9781611972788.69

Robust clustering in arbitrarily oriented subspaces. / Achtert, Elke; Böhm, Christian; David, Jörn; Kröger, Peer; Zimek, Arthur.

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. ed. / Chid Apte; Haesun Park; Ke Wang; Mohammad J. Zaki. Vol. 2 Society for Industrial and Applied Mathematics, 2008. p. 763-774 (S I A M Proceedings in Applied Mathematics, Vol. 130).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Achtert E, Böhm C, David J, Kröger P, Zimek A. Robust clustering in arbitrarily oriented subspaces. In Apte C, Park H, Wang K, Zaki MJ, editors, Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. Vol. 2. Society for Industrial and Applied Mathematics. 2008. p. 763-774. (S I A M Proceedings in Applied Mathematics, Vol. 130). https://doi.org/10.1137/1.9781611972788.69