Learning multiple nonredundant clusterings
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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