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Dimension Reduction Techniques for Clustering

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Encyclopedia of Database Systems

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Synonyms

Graph embedding; Subspace selection

Definition

High dimensional datasets is frequently encountered in data mining and statistical learning. Dimension reduction eliminates noisy data dimensions and thus and improves accuracy in classification and clustering, in addition to reduced computational cost. Here the focus is on unsupervised dimension reduction. The wide used technique is principal component analysis which is closely related to K-means cluster. Another popular method is Laplacian embedding which is closely related to spectral clustering.

Historical Background

Principal component analysis (PCA) was introduced by Pearson in 1901 and formalized in 1933 by Hotelling. PCA is the foundation for modern dimension reduction. A large number of linear dimension reduction techniques were developed during 1950–1970s.

Laplacian graph embedding (also called quadratic placement) is developed by Hall [8] in 1971. Spectral graph partitioning [6], is initially studied in 1970s; it is...

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Author information

Authors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Chris Ding

Authors
  1. Chris Ding
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Corresponding author

Correspondence to Chris Ding .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science and Engineering, The University of California at Riverside, Bourns College of Engineering, 92521, Riverside, CA, USA

    Dimitrios Gunopulos

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© 2016 Springer Science+Business Media New York

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Ding, C. (2016). Dimension Reduction Techniques for Clustering. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_612-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_612-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

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