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Modified global k-means algorithm for minimum sum-of-squares clustering problems. Presenter : Lin, Shu -Han Authors : Adil M. Bagirov. Pattern Recognition (PR, 2008). Outline. Motivation Objective Methodology Experiments Conclusion Comments. Motivation. k- Means algorithm

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modified global k means algorithm for minimum sum of squares clustering problems

Modified global k-means algorithm forminimum sum-of-squares clustering problems

Presenter : Lin, Shu-Han

  • Authors : Adil M. Bagirov

Pattern Recognition (PR, 2008)

outline
Outline
  • Motivation
  • Objective
  • Methodology
  • Experiments
  • Conclusion
  • Comments
motivation
Motivation
  • k-Meansalgorithm
    • sensitive to the choice of starting points
    • inefficient for solving clustering problems in large data sets
  • Global k-Means (GKM) algorithm
    • incremental algorithm (dynamically adds a cluster center at a time)
    • uses each data point as a candidate for the k-th cluster center
objectives
Objectives

Propose a new version of GKM

methodology k means
Methodology– k-Means

sensitive to the choice of a starting point

5

methodology objective function
Methodology– Objectivefunction
  • Oldversion
  • Reformulatedversion

7

methodology fast gkm algorithm
Methodology– fast GKM algorithm
  • Oldversion
  • Proposedversion(auxiliaryclusterfunction)

8

experiments
Experiments

MSk-means:Multi-startk-means

GKM:fastGlobalK-Means

MGKM:ModifiedGlobalK-Means

11

experiments2
Experiments
  • Overall(14datasets,140results)
    • The MS k-meansalgorithm finds the best known (or near best known) solutions42 (33.3%) times
    • GKMalgorithm 76 (60.3%) times
    • MGKMalgorithm 102 (81.0%) times
  • Largekinlargedatasets(m)
    • The MS k-means algorithmfailedto find the best known (or near best known) solutions
    • GKM algorithmfinds such solutions 22 (45.8%) times
    • MGKM algorithm42(87.5%) times.

13

conclusions
Conclusions
  • AnewversionoftheGKM
    • Changethecomputationofstartingpoints
    • Byminimizetheauxiliaryclusterfunction
    • Giventolerance
    • IsmoreeffectivethanGKM
      • largedatasetespecially
  • Thechoiceofstartingpointsink-meansiscrucial
comments
Comments
  • Advantage
    • Theoreticallyanalysis
  • Drawback
    • Describewhytheythinktomodifyanythingtheytendtomodifyisimportant,orneedto.
  • Application
    • GKMoutperformsk-meansalgorithm
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