An Improved Frequent Pattern Tree Based Association Rule Mining Technique

Abstract

Discovery of association rules among the large number of item sets is considered as an important aspect of data mining. The ever increasing demand of finding pattern from large data enhances the association rule mining. Researchers developed a lot of algorithms and techniques for determining association rules. The main problem is the generation of candidate set. Among the existing techniques, the frequent pattern growth (FP-growth) method is the most efficient and scalable approach. It mines the frequent item set without candidate set generation. The main obstacle of FP growth is, it generates a massive number of conditional FP tree. In this research paper, we proposed a new and improved FP tree with a table and a new algorithm for mining association rules. This algorithm mines all possible frequent item set without generating the conditional FP tree. It also provides the frequency of frequent items, which is used to estimate the desired association rules