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    題名: Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold
    作者: Chne, Chun-hao;Li, Ai-fang;Lee, Yeong-chyi;Hong, Tzung-pei
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: data mining;fuzzy association rules;fuzzy coherent rules;fuzzy set;membership function
    日期: 2012-06-13
    上傳時間: 2012-08-22 17:06:43 (UTC+8)
    出版者: IEEE
    摘要: Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.
    關聯: Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, pp.1779-1783
    DOI: 10.1109/FUZZ-IEEE.2012.6251309
    顯示於類別:[資訊工程學系暨研究所] 會議論文

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