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