Frequent pattern mining is one of the active research themes in data mining. It plays an important role in all data mining tasks such as clustering, classification, prediction, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of patterns generated. A reasonable solution is identifying efficient method to finding frequent patterns without candidate generation. In this paper, we present An Evolutionary algorithm for mining association rules using Boolean approach for mining association rules in large databases of sales transactions. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm is proposed in this paper. This new algorithm scanning the database once and avoiding generating candidate itemsets in computing frequent itemset and adopts a Boolean vector “relational calculus” method to discovering frequent itemsets. Experimental results show that this algorithm can quickly discover frequent itemsets and effectively mine potential association rules.
Comments: 6 Pages.
[v1] 2012-08-18 21:32:21
Unique-IP document downloads: 256 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.