%0 Journal Article
%T Reduce Scanning Process for Efficient Mining Tree in Association Rule Mining
%A Richa Sharma
%A Mr. Premnarayan Arya
%J International Journal of Technological Exploration and Learning
%D 2012
%I Nexus2world Publication
%X The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using a Array-based structure, known as a FP-tree, for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But In FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel ABFP tree technique that greatly reduces the need to traverse FP-trees and array 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 FP-tree data structure in combination with the FP- Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.
%K FP-Tree
%K WSFP ¨CTree
%K Frequent Patterns
%K Array Technique.
%U http://ijtel.org/v1n3/(74-77)CRP_0103P20.pdf