A Fast Minimal Infrequent Itemset Mining Algorithm

TL;DR

This paper introduces a new fast algorithm for mining minimal infrequent itemsets, significantly improving runtime and scalability on large datasets compared to existing methods.

Contribution

The paper presents a novel algorithm that enhances the efficiency and scalability of infrequent itemset mining in large datasets.

Findings

Substantial reduction in run-time compared to previous algorithms

Effective scalability to datasets with several million records

Demonstrated performance improvements across diverse datasets

Abstract

A novel fast algorithm for finding quasi identifiers in large datasets is presented. Performance measurements on a broad range of datasets demonstrate substantial reductions in run-time relative to the state of the art and the scalability of the algorithm to realistically-sized datasets up to several million records.