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Information-Theoretic Non-redundant Subspace Clustering

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Book cover Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

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Abstract

A comprehensive understanding of complex data requires multiple different views. Subspace clustering methods open up multiple interesting views since they support data objects to be assigned to different clusters in different subspaces. Conventional subspace clustering methods yield many redundant clusters or control redundancy by difficult to set parameters. In this paper, we employ concepts from information theory to naturally trade-off the two major properties of a subspace cluster: The quality of a cluster and its redundancy with respect to the other clusters. Our novel algorithm NORD (for NOn-ReDundant) efficiently discovers the truly relevant clusters in complex data sets without requiring any kind of threshold on their redundancy. NORD also exploits the concept of microclusters to support the detection of arbitrarily-shaped clusters. Our comprehensive experimental evaluation shows the effectiveness and efficiency of NORD on both synthetic and real-world data sets and provides a meaningful visualization of both the quality and the degree of the redundancy of the clustering result on first glance.

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Notes

  1. 1.

    http://lib.stat.cmu.edu/datasets/CPS_85_Wages.

  2. 2.

    http://www.exploredata.net/Downloads/Gene-Expression-Data-Set.

  3. 3.

    http://dme.rwth-aachen.de/de/OpenSubspace/RESCU.

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Author information

Authors and Affiliations

  1. Technical University of Munich, Bavaria, Germany

    Nina Hubig

  2. University of Vienna, Vienna, Austria

    Claudia Plant

Authors
  1. Nina Hubig
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  2. Claudia Plant
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Corresponding author

Correspondence to Nina Hubig .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Hubig, N., Plant, C. (2017). Information-Theoretic Non-redundant Subspace Clustering. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-57454-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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