(untitled) (Global Journal of Advanced Engineering Technologies and Sciences, 2016)
In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale systems in supermarkets In this paper we present a novel Qbaesd FP tree technique that greatly reduces the need to traverse FP-trees and Q based FP tree, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for sparse datasets. We then present a new algorithm which use the Q FP-tree data structure in combination with the FPExperimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.