Article

Finding hierarchies of subspace clusters

Authors:

Arthur ZimekAuthors Info & Claims

ECMLPKDD'06: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases

Pages 446 - 453

Published: 18 September 2006 Publication History

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 subspace clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.

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Cited By

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Achtert EKriegel HSchubert EZimek ARoss KSrivastava DPapadias D(2013)Interactive data mining with 3D-parallel-coordinate-treesProceedings of the 2013 ACM SIGMOD International Conference on Management of Data10.1145/2463676.2463696(1009-1012)Online publication date: 22-Jun-2013
https://dl.acm.org/doi/10.1145/2463676.2463696
Gullo FDomeniconi CTagarelli ASellis TMiller RKementsietsidis AVelegrakis Y(2011)Advancing data clustering via projective clustering ensemblesProceedings of the 2011 ACM SIGMOD International Conference on Management of data10.1145/1989323.1989400(733-744)Online publication date: 12-Jun-2011
https://dl.acm.org/doi/10.1145/1989323.1989400
Günnemann SKremer HLenhard DSeidl T(2011)Subspace clustering for indexing high dimensional dataProceedings of the 14th International Conference on Extending Database Technology10.1145/1951365.1951395(237-248)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1951365.1951395
Show More Cited By

Finding hierarchies of subspace clusters
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

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Published In

ECMLPKDD'06: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases

September 2006

658 pages

ISBN:3540453741

Editors:
Johannes Fürnkranz
Technische Universität Darmstadt, Fachbereich Informatik, Darmstadt, Germany
,
Tobias Scheffer
Humboldt-Universität zu Berlin, Institut für Informatik, Berlin, Germany
,
Myra Spiliopoulou
Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Magdeburg, Germany

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 September 2006

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Achtert EKriegel HSchubert EZimek ARoss KSrivastava DPapadias D(2013)Interactive data mining with 3D-parallel-coordinate-treesProceedings of the 2013 ACM SIGMOD International Conference on Management of Data10.1145/2463676.2463696(1009-1012)Online publication date: 22-Jun-2013
https://dl.acm.org/doi/10.1145/2463676.2463696
Gullo FDomeniconi CTagarelli ASellis TMiller RKementsietsidis AVelegrakis Y(2011)Advancing data clustering via projective clustering ensemblesProceedings of the 2011 ACM SIGMOD International Conference on Management of data10.1145/1989323.1989400(733-744)Online publication date: 12-Jun-2011
https://dl.acm.org/doi/10.1145/1989323.1989400
Günnemann SKremer HLenhard DSeidl T(2011)Subspace clustering for indexing high dimensional dataProceedings of the 14th International Conference on Extending Database Technology10.1145/1951365.1951395(237-248)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1951365.1951395
Achtert EKriegel HZimek A(2008)ELKIProceedings of the 20th international conference on Scientific and Statistical Database Management10.1007/978-3-540-69497-7_41(580-585)Online publication date: 9-Jul-2008
https://dl.acm.org/doi/10.1007/978-3-540-69497-7_41
Achtert EBöhm CKriegel HKröger PMüller-Gorman IZimek A(2007)Detection and visualization of subspace cluster hierarchiesProceedings of the 12th international conference on Database systems for advanced applications10.5555/1783823.1783843(152-163)Online publication date: 9-Apr-2007
https://dl.acm.org/doi/10.5555/1783823.1783843

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Finding the Optimal Subspace for Clustering

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Efficient approaches for summarizing subspace clusters into k representatives

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