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

TL;DR

This paper proposes an improved FP Tree algorithm with a modified header table and a spare table, enhancing efficiency by avoiding candidate set and CP tree generation in association rule mining.

Contribution

It introduces a novel FP Tree-based method with a modified header table and MFI algorithm that improves efficiency in frequent item set generation.

Findings

Reduces the number of CP trees generated.

Enhances the speed of association rule mining.

Avoids candidate set generation, improving scalability.

Abstract

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.