Frequent-Itemset Mining using Locality-Sensitive Hashing

TL;DR

This paper explores the use of Locality-Sensitive Hashing (LSH) to improve frequent itemset mining by reducing I/O operations and candidate generation, proposing randomized Apriori variants based on LSH techniques.

Contribution

It introduces novel randomized Apriori algorithms utilizing asymmetric LSH over Hamming distance and Jaccard similarity to enhance efficiency.

Findings

Reduced I/O operations in frequent itemset mining

Lowered candidate generation through LSH-based methods

Maintained comparable accuracy with traditional Apriori

Abstract

The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Hamming distance and Jaccard similarity.