Learning multiple nonredundant clusterings

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Learning multiple nonredundant clusterings

Presenter : Wei-Hao Huang 　Authors : Ying Gui, Xiaoli Z. Fern, Jennifer G. DY

TKDD, 2010

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Outlines Motivation Objectives Methodology Experiments Conclusions Comments

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Motivation· Data exist multiple groupings that are reasonable

and interesting from different perspectives.· Traditional clustering is restricted to finding only

one single clustering.

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I. M.Objectives

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

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Methodology· Orthogonal clustering

─ Cluster space· Clustering in orthogonal subspaces

─ Feature space· Automatically Finding the number of clusters· Stopping criteria

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I. M.Orthogonal Clustering Framework

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X (Face dataset)

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I. M.Orthogonal clustering

· Residue space

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)

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I. M.Clustering in orthogonal subspaces· Feature space

─ linear discriminant analysis (LDA)

─ singular value decomposition (SVD)

─ LDA v.s. SVD where

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Projection Y=ATX

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I. M.Clustering in orthogonal subspaces

· Residue space

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A(t)= eigenvectors of

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I. M.Compare moethod1 and mothod2· Residue space· Moethod1

─ · Moethod2

─ ─

· Moethod1 is a special case of Moethod2.─

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A(t)= eigenvectors of

M’=M then P1=P2

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I. M.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

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I. M.Experiments· Evaluation

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I. M.Experiments· Synthetic

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I. M.Experiments· Face dataset

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I. M.Experiments· WebKB dataset

· Vowe phoneme dataset

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I. M.Experiments· Digit dataset

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I. M.Experiments· Finding the number of clusters

─ K-means Gap statistics

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I. M.Experiments· Finding the number of clusters

─ GMMBIC

· Stopping Criteria─ SSE is less than 10% at first iteration─ Kopt=1─ Kopt > Kmax Select Kmax ─ Gap statistics─ BIC Maximize value of BIC

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I. M.Experiments· Synthetic dataset

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I. M.Experiments· Face dataset

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I. M.Experiments· WebKB dataset

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Conclusions

• To discover varied interesting and meaningful

clustering solutions.

• Method2 is able to apply any clustering and

dimensionality reduction algorithm.

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Comments· Advantages

─ Find Multiple non-redundant clustering solutions

· Applications─ Data Clustering