Discovery of Maximal Frequent Item Sets using Subset Creation

TL;DR

This paper introduces a new efficient algorithm for directly discovering maximal frequent itemsets in data mining, improving over traditional methods that derive them indirectly and are time-consuming.

Contribution

The paper proposes a novel approach to directly find maximal frequent itemsets using subset concepts, reducing computational time compared to existing methods.

Findings

The proposed algorithm efficiently finds maximal frequent itemsets.

It reduces the time complexity of the mining process.

The method outperforms traditional algorithms in speed.

Abstract

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like utility, number of items sold and for the mining of association data patterns. In this paper, we propose an efficient algorithm to find maximal frequent itemset first. Most of the association rule algorithms used to find minimal frequent item first, then with the help of minimal frequent itemsets derive the maximal frequent itemsets, these methods consume more time to find maximal frequent itemsets. To overcome this problem, we propose a new approach to find maximal frequent itemset directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent itemsets.