| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 100 === 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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