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Research On The Application And Effection Of Fuzzy Implication Operators In Fuzzy Association Rules Mining

Posted on:2005-08-06Degree:MasterType:Thesis
Country:ChinaCandidate:Y GaoFull Text:PDF
GTID:2168360155455152Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
Association rule mining is one of the key points in the research field of data mining. To deal with the problem of sharp boundary in mining quantative association rules, fuzzy sets has been introduced to data mining. Such kind of association rule mining is called mining fuzzy association rule. Using degree of implication defined on fuzzy implication operator, the present work propose a new method to mine fuzzy association rule, and discuss the infection of different fuzzy implication operators on it. Conceretly, the main work includes:1. Firstly, we analyzed some properties of fuzzy association rules and gave the definition of redundant fuzzy association rules (RFA) . Then, using degree of implication on fuzzy implication operator, we introduced a new algorithm to mine fuzzy association rules from frequent itemsets. At last, an example was given to illustrate our method.2. Secondly, we compared some general methods of solving the problem of fuzzy association rule mining by degree of support and degree of implication. And we discussed the different situations when certain method should be used.3. Thirdly, we gave the definition of distance between two fuzzy association rule sets, and anylized the rationality of this definition.4. Fourthly, using the distance formula mentioned above, we did some cluster analysis of the 18 fuzzy association rules obtained from the measure of degree of implication defined by the 18 frequently used fuzzy association operators.
Keywords/Search Tags:data mining, association rule, fuzzy association rule, fuzzy implication operator, cluster analysis
PDF Full Text Request
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