| 摘要(英) |
Social network analysis utilizes the social messages and behaviors between users to analyze the relationships and characteristics of communities. We try to support recommending search engine system by discovering the hidden information to help increasing the precision when searching specific subject related contents. Nevertheless the result analyzed in the past may not always provide a proper or correct information, new documents posted in the future would definitely influence the appearance and structure of communities, users themselves may even have to be assigned to another different community.
In our research, we construct a special hybrid community structure which is assembled by several subject categories. With the documents shared by the users at the social network, we cluster similar categories with K-Means Clustering Algorithm according to the similarity (in our research we refer it as Fuzzy RT relation) between categories. With this clustering technique, we assign the users to the cluster which contains the subject category that they’re interested in. Considering the influence brought by the new documents in the future, we also employ an update scheme that is also based on K-Means clustering to adjust the structure if the communities.
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| 參考文獻 |
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[2] http://digg.com
[3] http://delicious.com
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[5] Wai-chiu Wang, Ada Wai-chee Fu, Incremental Document Clustering for Web Page Classification, Chinese University of Hong Kong,CiteSeer,2000
[6] Wikipedia – K-means clustering, http://en.wikipedia.org/wiki/K-means_clustering
[7] Open Directory Project http://www.dmoz.org
[8] Digg API, http://developers.digg.com/documentation
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[11] 郭依羚，「基於社群行為分析之階層化角色分類法」，國立中央大學，碩士論文，民國９９年。
[12] M.E.J.Newman and M.Girvan, Finding and evaluating community in networks, University of Michigan, Cornell University, 2004, The American Physical Society 2004.
[13] Shihua Zhang, Rius-Sgheng Wang, Xiang-Sun Zhang, Identification of overlapping community structure in complex networks using fuzzy c-means clustering, Renmin University of China, Beijing, China, ScienceDirect 2006.
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