Maximal frequent itemset generation using segmentation approach

TL;DR

This paper introduces a segmentation-based method for efficiently mining maximal frequent itemsets from large datasets, reducing computational effort compared to traditional algorithms.

Contribution

The paper proposes a novel segmentation approach for maximal frequent itemset generation that outperforms existing methods in efficiency.

Findings

Method outperforms existing algorithms in speed

Reduces number of itemsets to process

Effective on large data sources

Abstract

Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This problem can be solved by mining only the Maximal Frequent Itemsets (MFS). Maximal Frequent Itemsets are frequent itemsets which have no proper frequent superset. Thus, the generation of only maximal frequent itemsets reduces the number of itemsets and also time needed for the generation of all frequent itemsets as each maximal itemset of length m implies the presence of 2m-2 frequent itemsets. Furthermore, mining only maximal frequent itemset is sufficient in many data mining applications like minimal key discovery and theory extraction. In this paper, we suggest a novel method for finding the maximal frequent itemset from huge data sources using the concept of segmentation of data source and prioritization of segments. Empirical evaluation shows that this method outperforms various other known methods.