Weighted Approach to Projective Clustering

  • Przemysław Spurek
  • Jacek Tabor
  • Krzysztof Misztal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8104)

Abstract

k-means is the basic method applied in many data clustering problems. As is known, its natural modification can be applied to projection clustering by changing the cost function from the squared-distance from the point to the squared distance from the affine subspace. However, to apply thus approach we need the beforehand knowledge of the dimension.

In this paper we show how to modify this approach to allow greater flexibility by using the weights over respective range of subspaces.

Keywords

Projective clustering Karhunen-Loéve Transform PCA k-means 

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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Przemysław Spurek
    • 1
  • Jacek Tabor
    • 1
  • Krzysztof Misztal
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceJagiellonian UniversityKrakówPoland
  2. 2.Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland

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