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：2013
卷号：3
期号：4
出版社：S.S. Mishra
摘要：Frequent item generation is a key approach in association rule mining. The Data mining is the process o f generating frequent itemsets that satisfy minimum support. Efficient algorithms to mine frequent patterns are crucial in data mining. Since the Apriori algorithm was proposed to generate the frequent item sets, there have been several methods proposed to improve its performance. But they do not satisfy the ti me constraint. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive a ssociation rules. The Enhance apriori algorithm has proposed in this paper requires less time in comparison to apriori algorithm. So the time is reducing.
关键词：Apriori; Item set; Frequent Item set; Support count; threshold; Confidence
