Efficient Frequent Pattern Tree Construction

Association rule learning is a popular and well researched technique for discovering interesting relations between variables in large databases in the area of data mining. The association rules are a part of intelligent systems. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Apriori and FP-Growth algorithms are very familiar algorithms for association rule mining. In this paper we are more concentrated on the Construction of efficient frequent pattern trees. Here, we present the novel frequent pattern trees and the performance issues. The proposed trees are fast and efficient trees helps to extract the frequent patterns. This paper provides the major advantages in the FP-Growth algorithm for association rule mining with using the newly proposed approach.