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In todays world there is a wide availability of huge amount of data and thusthere is a need for turning this data into useful information which is referredto as knowledge. This demand for knowledge discovery process has led to thedevelopment of many algorithms used to determine the association rules. One ofthe major problems faced by these algorithms is generation of candidate sets.The FP Tree algorithm is one of the most preferred algorithms for associationrule mining because it gives association rules without generating candidatesets. But in the process of doing so, it generates many CP trees whichdecreases its efficiency. In this research paper, an improvised FP treealgorithm with a modified header table, along with a spare table and the MFIalgorithm for association rule mining is proposed. This algorithm generatesfrequent item sets without using candidate sets and CP trees.

An Improvised Frequent Pattern Tree Based Association Rule Mining Technique with Mining Frequent Item Sets Algorithm and a Modified Header Table