1 / 23

Learning multiple nonredundant clusterings

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.

gwen
Download Presentation

Learning multiple nonredundant clusterings

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related