期刊名称：Journal of Emerging Trends in Computing and Information Sciences
电子版ISSN：2079-8407
出版年度：2011
卷号：2
期号：12
页码：722-732
出版社：ARPN Publishers
摘要：The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a top-down progressive deepening method, developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle .
关键词：association rules; data mining; partition method; multilevel rules
