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：11
出版社：S.S. Mishra
摘要：Data mining is the analysis of large observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful for the owner. The data mining of association rules is an essential research aspect in the data mining fields. The association rules provide an effective means to found the potential link between the data, reflecting a built-in association between the data. Association rules are usually required to satisfy a user-specified minimum support and user-specified confidence at the same time. Association rule generation is usually split up into two separate steps: First, Minimum support is applied to find all frequent itemsets in a database. Second, these frequent itemsets and the minim um confidence constraints are used to form rules. While the second step is straight forward and first needs more attention. Apriori algorithm scans the database too many times. When the database storing a large number of data services, the limited memory capacity, and the system I/O load considerable time scanning the database will be a very long time, so efficiency is very low. In this paper, we introduce a new technique known as Hash-set technique that will help to increase the efficiency of algorithm by avoiding multiple scanning the database. Our Hash Techniques based algorithm is used to reduce the frequently scanning of item set also it helps to mine interesting association rules on the basis of lift and conviction technique.
关键词：Association rules; Apriori Algorithm; lift and conviction technique; Has-set technique; Data Mining
