期刊名称：Journal of Advanced Computer Science & Technology
印刷版ISSN：2227-4332
电子版ISSN：2227-4332
出版年度：2015
卷号：4
期号：1
页码：156-162
DOI：10.14419/jacst.v4i1.4398
出版社：Science Publishing Corporation
摘要：In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.
关键词：Data Mining;Knowledge Discovery in Database (KDD);Association Rule Mining;Interestingness Measures.
