AN IMPROVED ASSOCIATION RULE MINING WITH FP TREE USING POSITIVE AND NEGATIVE INTEGRATION
Rashmi Shikhariya, Nitin Shukla · Journal of Global Research in Computer Sciences · 2012
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.