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Scholars Journal of Engineering and Technology | Volume-1 | Issue-02
Efficient Mining Tree in Association Rule Mining for Reducing the Time complexity
Richa Sharma, Pream Narayan Araya
Published: Feb. 26, 2013 |
101
64
DOI: 10.36347/sjet
Pages: 63-67
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Abstract
Association rule mining, one of the most important and well researched techniques of data mining, was first
introduced in. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of
items in the transaction databases or other data repositories. However, no method has been shown to be able to handle
data streams, as no method is scalable enough to manage the high rate which stream data arrive at. More recently, they
have received attention from the data mining community and methods have been defined to automatically extract and
maintain gradual rules from numerical databases. In this paper, we thus propose an original approach to mine data
streams for Association rule mining. 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 FPtree 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