Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining

TL;DR

This paper proposes an improved Apriori algorithm using a frequent pattern tree to enhance efficiency in real-time data mining applications by reducing database scans.

Contribution

It introduces a novel approach that overcomes the original Apriori algorithm's limitations of time and space complexity through a pattern tree structure.

Findings

Reduced number of database scans

Improved processing time for large datasets

Enhanced suitability for real-time applications

Abstract

Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. First, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and present an improvement on Apriori.