Association Rule Mining of Personal Hobbies in Social Networks

Xiaoqing Yu, Shimin Miao, Huanhuan Liu, Jenq-Neng Hwang, Wanggen Wan and Jing Lu
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Xiaoqing Yu: School of Communication and Information Engineering Shanghai University, Shanghai, China
Shimin Miao: School of Communication and Information Engineering Shanghai University, Shanghai, China
Huanhuan Liu: School of Communication and Information Engineering, Shanghai University, Shanghai, China
Jenq-Neng Hwang: Department of Electrical Engineering, University of Washington, Seattle WA, USA
Wanggen Wan: School of Communication and Information Engineering, Shanghai University, Shanghai, China
Jing Lu: School of Communication and Information Engineering Shanghai University, Shanghai, China

International Journal of Web Services Research (IJWSR), 2017, vol. 14, issue 1, 13-28

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:14:y:2017:i:1:p:13-28

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