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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17393


    Title: A Fuzzy Statistics based Method for Mining Fuzzy Correlation Rules
    Other Titles: 一種探勘模糊相關規則的模糊統計方法
    Authors: Lin, Nancy P.;Chen, Hung-jen;Chueh, Hao-en;Hao, Wei-hua;Chang, Chung-i
    Contributors: 淡江大學資訊工程學系;淡江大學軍訓室
    Keywords: Fuzzy association rules, Fuzzy itemsets, Fuzzy statistics, Fuzzy correlation analysis, Linear relationship, Fuzzy correlation rules
    Date: 2007-11
    Issue Date: 2013-03-12 11:22:39 (UTC+8)
    Publisher: Athens: The World Scientific and Engineering Academy and Society
    Abstract: Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzzy rule, in this paper, we construct a new method for finding relationships between fuzzy itemsets based on fuzzy statistics, and the generated rules are called fuzzy correlation rules. In our method, a fuzzy correlation analysis which can show us the strength and the type of the linear relationship between two fuzzy itemsets is used. By using thus fuzzy statistics analysis, the fuzzy correlation rules with the information about that two fuzzy not only frequently occur together in same records but also are related to each other can be generated.
    Relation: WSEAS Transactions on Mathematics 6(11), pp.852-858
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文
    [應用日語系] 專利

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