Efficient Candidacy Reduction For Frequent Pattern Mining

TL;DR

This paper introduces a new method for reducing candidate patterns in frequent pattern mining, significantly decreasing the number of candidates and comparisons needed, thereby improving efficiency in data mining tasks.

Contribution

It proposes a novel candidate head set (H) approach that effectively minimizes candidate patterns and support comparisons in frequent pattern mining.

Findings

Reduces the number of candidate patterns significantly.

Decreases the number of support comparisons needed.

Verifies accuracy through experimental results.

Abstract

Certainly, nowadays knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is a main step in knowledge discovery process. Meanwhile frequent patterns play central role in data mining tasks such as clustering, classification, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of candidate patterns. For the past decade there have been an increasing number of efficient algorithms to mine the frequent patterns. However reducing the number of candidate patterns and comparisons for support counting are still two problems in this field which have made the frequent pattern mining one of the active research themes in data mining. A reasonable solution is identifying a small candidate pattern set from which can generate all frequent patterns. In this paper, a method is proposed based on a new candidate set called candidate head set or H which forms a small set of candidate patterns. The experimental results verify the accuracy of the proposed method and reduction of the number of candidate patterns and comparisons.