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Dynamic hierarchical algorithms for document clustering

Dynamic hierarchical algorithms for document clustering. Presenter : Wei- Hao Huang Authors : Reynaldo Gil- García , Aurora Pons- Porrata PRL, 2010. Outlines. Motivation Objectives Hierarchical clustering Methodology Experiments Conclusions Comments. Motivation.

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Dynamic hierarchical algorithms for document clustering

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  1. Dynamic hierarchical algorithms for document clustering Presenter : Wei-Hao Huang Authors : Reynaldo Gil-García, Aurora Pons-Porrata PRL, 2010

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

  3. Motivation • The World Wide Web and the number of text documentsmanaged in organizational intranets continue to grow at an amazing speed. • In dynamic information environments is usually desirable to apply adaptive methods for document organization such as clustering.

  4. Objectives • Static clustering methods mainly rely on having the whole collection ready before applying the algorithm. • dynamic algorithms able to update the clustering without perform complete reclustering. • Independent on the data order.

  5. Hierarchical clustering Agglomerative and divisive Provide data-views at different levels

  6. Methodology • Dynamic hierarchical agglomerative framework • Specific algorithm: • Dynamic hierarchical compact (DHC) • Create disjoint hierarchies of clusters • Dynamic hierarchical star (DHS) • Produce overlapped hierarchies

  7. Dynamic hierarchical agglomerative framework j i β-similarity β is minimum similarity threshold i is a β-isolated cluster if its similarity with all clusters < β i is β-similarity j, if their similarity >= β

  8. Dynamic hierarchical agglomerative framework

  9. Updating of the max-S graph

  10. Dynamic hierarchical compact: Connected component cover

  11. Dynamic hierarchical star:Star cover updating

  12. Experiments Using 15 benchmark text collection. Clustering quality Sensitivity to parameters Balance Efficiency

  13. Clustering quality- Overall F1 measure

  14. Clustering quality- FCubed measure

  15. Clustering quality- HF1

  16. Sensitivity to parameters

  17. Depth and width of the hierarchies

  18. Efficiency

  19. Conclusions • Methods are suitable for producing hierarchical clustering solutions in dynamic environments effectively and efficiently. • Better balance between depth and width. • Offer hierarchies easier to browse than traditional algorithms.

  20. Comments • Advantages • Deal with dynamic data sets. • Effectiveness and the efficiency of the clustering. • Applications • Hierarchical clustering

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