An optimized algorithm for association rule mining using FP tree

Data mining is used to deal with the huge size of the data stored in the database to extract the desired information and knowledge. It has various techniques for the extraction of data; association rule mining is the most effective data mining technique among them. It discovers hidden or desired pattern from large amount of data. Among the existing techniques the frequent pattern growth (FP growth) algorithm is the most efficient algorithm in finding out the desired association rules. It scans the database only twice for the processing. The issue with the FP growth algorithm is that it generates a huge number of conditional FP trees. In the proposed work, we have designed a new technique which mines out all the frequent item sets without the generation of the conditional FP trees. Unlike FP tree it scans the database only once which reduces the time efficiency of the algorithm. It also finds out the frequency of the frequent item sets to find out the desired association rules. (C) 2015 The Authors. Published by Elsevier B.V.