文章基本信息

标题：An Efficient Algorithm for Mining Multilevel Association Rule Based on Pincer Search
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作者：Pratima Gautam ; Rahul Shukla
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：4
出版社：IJCSI Press
摘要：Discovering frequent itemset is a key difficulty in significant data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. The problem of developing models and algorithms for multilevel association mining poses for new challenges for mathematics and computer science. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed princer search concepts, multilevel taxonomy and different minimum supports to find multilevel association rules in a given transaction data set. This search is used only for maintaining and updating a new data structure. It is used to prune early candidates that would normally encounter in the top-down search. A main characteristic of the algorithms is that it does not require explicit examination of every frequent itemsets, an example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
关键词：Association rules; data mining; multilevel association rule; princer algorithm