期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：5
页码：1856-1861
出版社：TechScience Publications
摘要：Existing research in association mining has focused mainly on how to expedite the search for frequently cooccurring groups of items in “shopping cart” type of transactions; less attention has been paid to methods that exploit these frequent itemsets for prediction purposes. This paper contributes to the latter task by proposing a technique that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by uncertainty processing techniques, including the classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination./
关键词：Frequent itemsets; itemset trees (IT-Tresss);uncertainty processing; Dempster-Shafer theory.d
