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Learning multiple nonredundant clusterings. Presenter : Wei- Hao Huang Authors : Ying Gui , Xiaoli Z. Fern, Jennifer G. DY TKDD, 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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learning multiple nonredundant clusterings

Learning multiple nonredundantclusterings

Presenter : Wei-Hao Huang

Authors : Ying Gui, Xiaoli Z. Fern, Jennifer G. DY

TKDD, 2010

outlines
Outlines
  • Motivation
  • Objectives
  • Methodology
  • Experiments
  • Conclusions
  • Comments
motivation
Motivation
  • Data exist multiple groupings that are reasonable and interesting from different perspectives.
  • Traditional clustering is restricted to finding only one single clustering.
objectives
Objectives
  • To propose a new clustering paradigm for finding all non-redundant clustering solutions of the data.
methodology
Methodology
  • Orthogonal clustering
    • Cluster space
  • Clustering in orthogonal subspaces
    • Feature space
  • Automatically Finding the number of clusters
  • Stopping criteria
orthogonal clustering
Orthogonal clustering

)

Residue space

clustering in orthogonal subspaces
Clustering in orthogonal subspaces

Projection Y=ATX

  • Feature space
    • linear discriminant analysis (LDA)
    • singular value decomposition (SVD)
    • LDA v.s. SVD
      • where
clustering in orthogonal subspaces1
Clustering in orthogonal subspaces

A(t)= eigenvectors of

Residue space

compare moethod1 and mothod2
Compare moethod1 and mothod2

A(t)= eigenvectors of

M’=M then P1=P2

  • Residue space
  • Moethod1
  • Moethod2
  • Moethod1 is a special case of Moethod2.
experiments
Experiments
  • To use PCA to reduce dimensional
  • Clustering
    • K-means clustering
      • Smallest SSE
    • Gaussian mixture model clustering (GMM)
      • Largest maximum likelihood
  • Dataset
    • Synthetic
    • Real-world
      • Face, WebKB text, Vowel phoneme, Digit
experiments1
Experiments

Evaluation

experiments2
Experiments

Synthetic

experiments3
Experiments

Face dataset

experiments4
Experiments

WebKB dataset

Vowe phoneme dataset

experiments5
Experiments

Digit dataset

experiments6
Experiments
  • Finding the number of clusters
    • K-means  Gap statistics
experiments7
Experiments
    • Finding the number of clusters
    • GMMBIC
  • Stopping Criteria
    • SSE is less than 10% at first iteration
    • Kopt=1
    • Kopt> Kmax Select Kmax
    • Gap statistics
    • BIC Maximize value of BIC
experiments8
Experiments

Synthetic dataset

experiments9
Experiments

Face dataset

experiments10
Experiments

WebKB dataset

conclusions
Conclusions
  • To discover varied interesting and meaningful clustering solutions.
  • Method2 is able to apply any clustering and dimensionality reduction algorithm.
comments
Comments
  • Advantages
    • Find Multiple non-redundant clustering solutions
  • Applications
    • Data Clustering
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