期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：7
出版社：S.S. Mishra
摘要：Mining Association Rules means that, given a set of sales transactions, to discover all association among items such that the presence of some items in transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large item sets. Interesting patterns often occur at different levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation caused by a low minimum support. A better solution lies in exploiting support constraints, which specify what minimum support is required for what item sets, so that only the necessary item sets are generated. The support constraints are "pushed" into the Apriori item set generation so that the "best" minimum support is determined for each items et at runtime to preserve the essence of Apriori. This strategy is called Adaptive Apriori
关键词：Data Mining; association rules; domain-specific constraints;classification
