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：9
出版社：S.S. Mishra
摘要：Data mining is considered to deal with huge amounts of data w hich are kept in the database, to loca te required information and facts. Innovation of association rules a mong the huge number of item sets is observed as a significant feature of data mining. The always growing demand of finding pattern from huge data improves the association rule mining. The main purpose of data mining provides superior result for using knowledge base system. Researchers presented a lot of approaches and algorithms for determining association rules. This paper discusses few approaches for mining association rules. Association rule mining approach is the most efficient data mining method to find out hidden or required pattern among the large volume of data. It is responsible to find correlation relationships among various data attributes in a huge set of items in a database. Studying Apriori algorithm, it is a illustration of an enhanced association rule mining algorithm, which supports to avoid the replication of sa me items. This paper discusses an enhanced version of Apriori algorithm that is concentrated on four characteristics namely, First data preparation and chooses the desired data, second produce itemsets that decides the rule constraints for knowledge, third mine k-frequent itemsets using the new database and fourth produce the association rule that sets up the knowledge base and offer better results. Another approach discussed in this paper are the HASH MAPPING TABLE and HASH_TREE tactics used to optimize space complexity and time complexity
关键词：Data Mining; Association rules; Apriori algorithm; HASH_TREE; HMT
