Title: Method based on rough set for mining multi-dimensional association rules
Abstract: It is very time-consuming to discover association rules from the mass of data,and not all the rules are attractive to the user,so a lot of irrelevant information to the user's requirements may be generated when traditional mining methods are applied.In addition,most of the existing algorithms are for discovering one-dimensional association rules.Therefore,the authors defined a mining language which allowed users to specify items of interest to the association rules,as well as the parameters(for example,support,confidence,etc.).A method based on rough set theory for multi-dimensional association rules mining was also proposed,which dynamically generated frequent item sets and multi-dimensional association rules,and reduced the search space to generate frequent item sets.Finally,an example verifies the feasibility and effectiveness of the method.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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