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Learning multiple nonredundant clusterings

This research project explores a novel clustering paradigm to discover various non-redundant clustering solutions in data. The proposed Orthogonal Clustering Framework uses orthogonal subspaces and feature spaces to find multiple meaningful groupings. By combining methods like linear discriminant analysis and singular value decomposition, this approach automates the identification of the number of clusters and ensures non-redundancy in the results. Experimental evaluations on synthetic and real-world datasets demonstrate the effectiveness of the framework in discovering diverse and interesting clustering solutions. The methodology allows for flexibility in applying different clustering and dimensionality reduction algorithms. Overall, this framework offers a valuable tool for data clustering with applications in various domains.

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Learning multiple nonredundant clusterings

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  1. Learning multiple nonredundantclusterings Presenter : Wei-Hao Huang Authors : Ying Gui, Xiaoli Z. Fern, Jennifer G. DY TKDD, 2010

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Data exist multiple groupings that are reasonable and interesting from different perspectives. • Traditional clustering is restricted to finding only one single clustering.

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

  5. Methodology • Orthogonal clustering • Cluster space • Clustering in orthogonal subspaces • Feature space • Automatically Finding the number of clusters • Stopping criteria

  6. Orthogonal Clustering Framework X (Face dataset)

  7. Orthogonal clustering ) Residue space

  8. Clustering in orthogonal subspaces Projection Y=ATX • Feature space • linear discriminant analysis (LDA) • singular value decomposition (SVD) • LDA v.s. SVD • where

  9. Clustering in orthogonal subspaces A(t)= eigenvectors of Residue space

  10. Compare moethod1 and mothod2 A(t)= eigenvectors of M’=M then P1=P2 • Residue space • Moethod1 • Moethod2 • Moethod1 is a special case of Moethod2.

  11. 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

  12. Experiments Evaluation

  13. Experiments Synthetic

  14. Experiments Face dataset

  15. Experiments WebKB dataset Vowe phoneme dataset

  16. Experiments Digit dataset

  17. Experiments • Finding the number of clusters • K-means  Gap statistics

  18. 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

  19. Experiments Synthetic dataset

  20. Experiments Face dataset

  21. Experiments WebKB dataset

  22. Conclusions • To discover varied interesting and meaningful clustering solutions. • Method2 is able to apply any clustering and dimensionality reduction algorithm.

  23. Comments • Advantages • Find Multiple non-redundant clustering solutions • Applications • Data Clustering

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