Association Rule Mining of Personal Hobbies in Social Networks

In this paper, the authors propose an effective scheme for association rule mining of personal hobbies in social networks. By introducing the connection and clipping techniques, the authors are able to ignore unrelated items in the process of finding frequent itemsets, resulting in more accurate candidate itemsets. More specifically, set operations, which are used in the process of combining frequent itemsets, can dramatically reduce the number of databases visited. Furthermore, to explore more practical rules, interestingness level is also introduced to eliminate rules that few people are interested in. The authors' proposed association rule mapping is shown to be able to provide new insights for supporting personalized services and virtual marketing.