Research Article Open Access

AN IMPROVED ASSOCIATION RULE MINING WITH FP TREE USING POSITIVE AND NEGATIVE INTEGRATION

Abstract

Construction and development of classifier that work with more accuracy and perform efficiently for large database is one of the key task of data mining techniques [l7] [18]. Training dataset repeatedly produces massive amount of rules. It‟s very tough to store, retrieve, prune, and sort a huge number of rules proficiently before applying to a classifier[1]. In such situation FP is the best choice but problem with this approach is that it generates redundant FP Tree. A Frequent Pattern Tree (FP-Tree) is a type of prefix tree [3] that allows the detection of recurrent (frequent) item set exclusive of the candidate item set generation [14]. It is anticipated to recuperate the flaw of existing mining methods. FP –Trees pursues the divide and conquers tactic. In this paper we have adopt the same idea of author [17] to deal with large database. For this we have integrated a positive and negative rule mining concept with frequent pattern (FP) of classification. Our method performs well and produces unique rules without ambiguity.

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