Finding hierarchies of subspace clusters

Elke Achtert*, Christian Böhm, Hans Peter Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek

*Corresponding author for this work

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

Abstract

Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional sub-space clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.

Original languageEnglish
Title of host publicationKnowledge Discovery in Databases : PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
EditorsJ. Fürnkranz, T. Scheffer, M. Spiliopoulou
PublisherSpringer
Publication date2006
Pages446-453
ISBN (Print)978-3-540-45374-1
ISBN (Electronic)978-3-540-46048-0
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 - Berlin, Germany
Duration: 18. Sep 200622. Sep 2006

Conference

Conference10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006
CountryGermany
CityBerlin
Period18/09/200622/09/2006
SponsorDeutsche Forschungsgemeinschaft, DFG, et al., Google Inc., Humboldt University of Berlin, IBM Corp, PASCAL
SeriesLecture Notes in Computer Science
Volume4213
ISSN0302-9743

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Clustering algorithms
Experiments

Cite this

Achtert, E., Böhm, C., Kriegel, H. P., Kröger, P., Müller-Gorman, I., & Zimek, A. (2006). Finding hierarchies of subspace clusters. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 446-453). Springer. Lecture Notes in Computer Science, Vol.. 4213 https://doi.org/10.1007/11871637_42
Achtert, Elke ; Böhm, Christian ; Kriegel, Hans Peter ; Kröger, Peer ; Müller-Gorman, Ina ; Zimek, Arthur. / Finding hierarchies of subspace clusters. Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. editor / J. Fürnkranz ; T. Scheffer ; M. Spiliopoulou. Springer, 2006. pp. 446-453 (Lecture Notes in Computer Science, Vol. 4213).
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abstract = "Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional sub-space clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.",
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Achtert, E, Böhm, C, Kriegel, HP, Kröger, P, Müller-Gorman, I & Zimek, A 2006, Finding hierarchies of subspace clusters. in J Fürnkranz, T Scheffer & M Spiliopoulou (eds), Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer, Lecture Notes in Computer Science, vol. 4213, pp. 446-453, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006, Berlin, Germany, 18/09/2006. https://doi.org/10.1007/11871637_42

Finding hierarchies of subspace clusters. / Achtert, Elke; Böhm, Christian; Kriegel, Hans Peter; Kröger, Peer; Müller-Gorman, Ina; Zimek, Arthur.

Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. ed. / J. Fürnkranz; T. Scheffer; M. Spiliopoulou. Springer, 2006. p. 446-453 (Lecture Notes in Computer Science, Vol. 4213).

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

TY - GEN

T1 - Finding hierarchies of subspace clusters

AU - Achtert, Elke

AU - Böhm, Christian

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AU - Müller-Gorman, Ina

AU - Zimek, Arthur

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N2 - Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional sub-space clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.

AB - Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional sub-space clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.

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SN - 978-3-540-45374-1

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Achtert E, Böhm C, Kriegel HP, Kröger P, Müller-Gorman I, Zimek A. Finding hierarchies of subspace clusters. In Fürnkranz J, Scheffer T, Spiliopoulou M, editors, Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer. 2006. p. 446-453. (Lecture Notes in Computer Science, Vol. 4213). https://doi.org/10.1007/11871637_42