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Multilevel Association Rules By Mining both Positive and Negative Approach

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dc.contributor.author Htun, May Thu
dc.date.accessioned 2019-08-04T16:58:30Z
dc.date.available 2019-08-04T16:58:30Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1701
dc.description.abstract Association rule mining is one of the key issues in knowledge discovery. The discovery of frequent patterns, association, and correlation relationship among huge amounts of data is useful in selective marketing, decision analysis and business management. Association rules are traditionally defined as implications of the form A=>B, where A and B are frequent itemsets in a transaction database. The method extends traditional associations to include association rules of forms A => ¬B, ¬ A => B, and ¬ A => ¬ B, which indicate negative associations between itemsets. The negative rules are generated from infrequent itemsets. This system generates the set of frequent itemsets and the set of infrequent itemsets with three database including books, electronic and grocery store of sale transaction. This system presents a method for mining both positive and negative association rules. This system demonstrates that experimental results and efficiency of both positive and negative association rules. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Multilevel Association Rules By Mining both Positive and Negative Approach en_US
dc.type Article en_US


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