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    題名: Applying cluster-based fuzzy association rules mining framework into EC environment
    作者: 邱宏彬;Chiu, Hung-Pin;Tang, Yi-Tsung;Hsieh, Kun-Lin
    貢獻者: 資訊管理學系
    關鍵詞: Fuzzy association rule;Clustering-based data mining;Electronic commerce (EC);Fuzzy membership functions
    日期: 2012-08
    上傳時間: 2019-02-22 15:24:25 (UTC+8)
    摘要: During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach.
    關聯: Applied Soft Computing
    vol. 12, no. 8
    pp.2114-2122
    顯示於類別:[資訊管理學系] 期刊論文

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