DOI: 10.36347/sjet

Pages: 63-67

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