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# Learning multiple nonredundant clusterings - PowerPoint PPT Presentation

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 nonredundantclusterings

Presenter : Wei-Hao Huang

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

TKDD, 2010

• Motivation

• Objectives

• Methodology

• Experiments

• Conclusions

• Data exist multiple groupings that are reasonable and interesting from different perspectives.

• Traditional clustering is restricted to ﬁnding only one single clustering.

• To propose a new clustering paradigm for ﬁnding all non-redundant clustering solutions of the data.

• Orthogonal clustering

• Cluster space

• Clustering in orthogonal subspaces

• Feature space

• Automatically Finding the number of clusters

• Stopping criteria

Orthogonal Clustering Framework

X (Face dataset)

Orthogonal clustering

)

Residue space

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 subspaces

A(t)= eigenvectors of

Residue space

A(t)= eigenvectors of

M’=M then P1=P2

• Residue space

• Moethod1

• Moethod2

• Moethod1 is a special case of Moethod2.

• 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

Evaluation

Synthetic

Face dataset

WebKB dataset

Vowe phoneme dataset

Digit dataset

• Finding the number of clusters

• K-means  Gap statistics

• 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

• Synthetic dataset

Face dataset

WebKB dataset

• To discover varied interesting and meaningful clustering solutions.

• Method2 is able to apply any clustering and dimensionality reduction algorithm.