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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91977


    Title: Mining customer knowledge for exploring online group buying behavior
    Authors: Liao, Shu-hsien;Chu, Pei-hui;Chen, Yin-ju;Chang, Chia-Chen
    Contributors: 淡江大學管理科學學系
    Keywords: Data mining;Association rules;Cluster analysis;Online group buying;Online group buying behavior
    Date: 2012-02-15
    Issue Date: 2013-08-12 13:24:13 (UTC+8)
    Publisher: Kidlington: Pergamon Press
    Abstract: Online group buying is an effective marketing method. By using online group buying, customers get unbelievable discounts on premium products and services. This not only meets customer demand, but also helps sellers to find new ways to sell products sales and open up new business models, all parties benefit in these transactions. During these bleak economic times, group buying has become extremely popular. Therefore, this study proposes a data mining approach for exploring online group buying behavior in Taiwan. Thus, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining, which is implemented for mining customer knowledge among online group buying customers in Taiwan. The results of knowledge extraction from data mining are illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to online group buying firms for future development.
    Relation: Expert Systems with Applications 39(3), pp.3708-3716
    DOI: 10.1016/j.eswa.2011.09.066
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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