Abstract
Mining academic social network is becoming increasingly necessary with the increasing amount of data. It is a favorite topic of research for many researchers. The data mining techniques are used for the mining of academic social networks. In this paper, we are presenting an efficient frequent item set mining technique for social academic network. The proposed framework first processes the research documents and then the enhanced frequent item set mining is applied to find the strength of relationship between the researchers. The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory space for computation purpose.
Highlights
Social networks are built upon social relations among people who share common interests, activities etc
Social networking sites represent members through profile pages which contain their personal information like homepage, fields of interest, hobbies, contact details etc
Many social networking sites offer search and mining services, by allowing a person to register and identify ties based on profile
Summary
Social networks are built upon social relations among people who share common interests, activities etc. Many social networking sites offer search and mining services, by allowing a person to register and identify ties based on profile Such networks can be briefly classified as informational, professional, educational, academic, news groups, sports based and so on. It is observed that collaborative effort by people across the globe makes research strong from different perspectives This proposed approach is based upon services offered by http://arnetminer.org which concentrates on accurately extracting researcher profile information from the web by integrating data from different sources. The necessity of Machine understandable data from Machine-readable data has lead to the specification of RDF framework It provides basis for analysing metadata and provides interoperability between applications that exchange information on the web.In this context people i.e. members of social network are referred as the resource. The implicit information within databases thereby extracted out by data mining helps in the unraveling of patterns that can be used in variety of applications
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