Reduce Scanning Process for Efficient Mining Tree in Association Rule Mining
Journal Title: The International Journal of Technological Exploration and Learning - Year 2012, Vol 1, Issue 3
Abstract
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.
Authors and Affiliations
Richa Sharma| S.A.T.I., Vidisha, India, Mr. Premnarayan Arya| S.A.T.I., Vidisha, India
Keywords
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