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Association Rule Mining of Personal Hobbies in Social Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaoqing Yu & Shimin Miao & Huanhuan Liu & Jenq-Neng Hwang & Wanggen Wan & Jing Lu, 2017. "Association Rule Mining of Personal Hobbies in Social Networks," International Journal of Web Services Research (IJWSR), IGI Global, vol. 14(1), pages 13-28, January.
    • Handle: RePEc:igg:jwsr00:v:14:y:2017:i:1:p:13-28
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