Improved Frequent Pattern Approach for Mining Association Rules
An important aspect of data mining is to discover association rules among large number of item sets. Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository. The main problem is the generation of candidate set. The Frequent Pattern growth (FP-growth) method is the most efficient and scalable approach among the existing techniques. 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. A new and improved FP tree with a table and a new algorithm for mining association rules is proposed.