Top Down Approach to find Maximal Frequent Item Sets using Subset Creation

TL;DR

This paper introduces a novel top-down method for directly finding maximal frequent itemsets using subset creation, improving efficiency over traditional bottom-up approaches.

Contribution

The paper presents a new approach that directly identifies maximal frequent itemsets, reducing computational time compared to existing algorithms that derive them from minimal frequent itemsets.

Findings

Proposed method is more efficient in finding maximal frequent itemsets.

Direct approach reduces time complexity compared to traditional methods.

Method effectively leverages subset concepts for faster results.

Abstract

Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maximal frequent itemset first. Most of the algorithms in literature 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 navel 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.