期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：6
页码：2857-2861
出版社：TechScience Publications
摘要：Social Network sites make it possible to search large amounts of data for characteristic rules and patterns. If applied to monitoring data recorded on a real time data or Business in a network, they can be used to post in the network site database. In this paper, we present “Supervised learning” , a method to cascade the decision tree learning methods to classify into either family oriented, comedy, romantic, horror activities in a social network site. we can be used to build any one of the decision tree such as (ID3, C4.5,CART), here the decision tree on each dataset refines the decision boundaries by learning the subgroups within the database. Our work studies the best algorithm by using classifying movies oriented activities in Social network database with supervised algorithms that have not been used before. We analyse the algorithm that have the best efficiency or the best learning and describes the proposed system of ID3 Decision Tree.
关键词：Supervised Learning; Social networks; Decision;Tree; Facebook; Database.ouse.ve
