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A prediction method of fuzzy association rules

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3 Author(s)
Jianjiang Lu ; Dept. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China ; Baowen Xu ; Jixiang Jiang

Quantitative attributes are partitioned into several fuzzy sets by c-means algorithm, and search technology of Apriori algorithm is improved to discover interesting fuzzy association rules. The first prediction method of fuzzy association rules is presented, and shortcoming of this prediction method is analyzed. Then, the second prediction method of fuzzy association rules with the variable threshold is presented. In the second prediction method, a little error between prediction value and actual value is allowed. When the error is less than a given threshold, prediction value is regarded as acceptable or rational. The second prediction method can obtain the different prediction precision corresponding to the different error threshold chosen by the users, so it is more flexible and effective that the first prediction method.

Published in:

Information Reuse and Integration, 2003. IRI 2003. IEEE International Conference on

Date of Conference:

27-29 Oct. 2003