文章基本信息

标题：Rough Set and Genetic based Model for Extracting Weighted Association Rules
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作者：Shrikant Brajesh Sagar ; Akhilesh Tiwari
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：11
页码：121-138
DOI：10.14257/ijhit.2015.8.11.10
出版社：SERSC
摘要：A novel approach for the efficient weighted association rule mining proposed in this present paper. The proposed approach reducts the transactional dataset (weighted) by utilizing the power of Rough Set theory. Furthermore, proposed approach acquires the benefit for weighted measures (w-support, w-confidence) for obtaining the most profitable weighted frequent itemsets and the Genetic Algorithm for the extracting the desired set of optimized weighted association rules. Experimental analysis of proposed approach has been done and observed that the approach works well and will be helpful in situation when there is a requirement for the consideration of extracting the best weighted association rules in decision-making process.
关键词：Weighted items; Rough Set Theory; Apriori Algorithm; min. w-support; min. ; w-confidence; weighted association rules; Genetic Algorithm