Presenter : Yu-Ting LU Authors: Christopher C. Yang and Tobun Dorbin Ng 2011. TSMCA
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Intelligent Database Systems 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
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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. - PowerPoint PPT Presentation
Transcript of Presenter : Yu-Ting LU Authors: Christopher C. Yang and Tobun Dorbin Ng 2011. TSMCA
Intelligent Database Systems Lab
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
Intelligent Database Systems Lab
Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
Methodology
Intelligent Database Systems Lab
Methodology-content clustering• Core Thread Concept Selection for Clustering
Intelligent Database Systems Lab
Methodology-content clustering• SDC Algorithm
Intelligent Database Systems Lab
Methodology-Interactive information visualization
Intelligent Database Systems Lab
Methodology-Interactive information visualization
Intelligent Database Systems Lab
Methodology-Interactive information visualization
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
Comments
• Advantages- SDC performs better than DBSCAN.- Effective noise filtering.
• Applications- Web opinions clustering.- Density-based clustering.
