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Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time


Akhilesh Dubey Aayush Mehta Akriti Saxena


Vol. 16  No. 5  pp. 109-112


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 Q-baesd 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 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.


FP-Tree, WSFP ?Tree, Frequent Patterns, Array Technique