Mining Frequent Itemsets Using Genetic Algorithm

TL;DR

This paper proposes using a genetic algorithm to efficiently discover all frequent itemsets in large datasets, aiming to reduce computational time compared to traditional methods.

Contribution

It introduces a novel application of genetic algorithms for mining frequent itemsets, emphasizing improved global search and reduced time complexity.

Findings

Genetic algorithm effectively finds all frequent itemsets.

The approach reduces computational time compared to traditional algorithms.

Global search capability enhances the discovery process.

Abstract

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm.