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Presenter : Yu-Ting LU Authors: Christopher C. Yang and Tobun Dorbin Ng 2011. TSMCA

Analyzing and Visualizing Web Opinion Development and Social Interactions with Density-Based Clustering. Presenter : Yu-Ting LU Authors: Christopher C. Yang and Tobun Dorbin Ng 2011. TSMCA. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Yu-Ting LU Authors: Christopher C. Yang and Tobun Dorbin Ng 2011. TSMCA

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  1. Analyzing and Visualizing Web Opinion Development and Social Interactions with Density-Based Clustering Presenter: Yu-Ting LUAuthors: Christopher C. Yang and TobunDorbin Ng2011. TSMCA

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

  3. Motivation • Analysis of developing Web opinions is potentially valuable for discovering ongoing topics. • Typical document clustering techniques with the goal of clustering all documents applied to Web opinions produce unsatisfactory performance.

  4. Objectives • We investigated the density-based clustering algorithm and proposed the scalable distance-based clustering technique for Web opinion clustering. • This Web opinion clustering technique enables the identification of themes within discussions in Web social networks and their development, as well as the interactions of active participants.

  5. Methodology

  6. Methodology-content clustering • Core Thread Concept Selection for Clustering

  7. Methodology-content clustering • SDC Algorithm

  8. Methodology-Interactive information visualization

  9. Methodology-Interactive information visualization

  10. Methodology-Interactive information visualization

  11. Experiments

  12. Experiments

  13. Experiments

  14. Conclusions • The SDC algorithm overcomes the weakness of DBSCAN algorithm by grouping less number of less relevant clusters together when they are density-reachable. • The result has shown that they are promising to extract clusters of threads with important topics and filter the noise.

  15. Comments • Advantages • SDC performs better than DBSCAN. • Effective noise filtering. • Applications • Web opinions clustering. • Density-based clustering.

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