# Frequent-Itemset Mining using Locality-Sensitive Hashing

arXiv: https://arxiv.org/abs/1603.01682 · HTML: https://mlsanity.com/paper/1603.01682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Minwise-Independent Permutations with Insertion and Deletion of Features](https://mlsanity.com/paper/2308.11240/) (2023)

## Abstract (arXiv)

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.