Frequent Itemset Mining using QUBO

TL;DR

This paper introduces a quantum computing approach to frequent itemset mining by approximating the problem as a maximum clique problem suitable for quantum annealing or QAOA, aiming to leverage quantum hardware for data mining tasks.

Contribution

It presents a novel R-step approximation method that reformulates frequent itemset mining as a maximum clique problem for quantum hardware implementation.

Findings

Demonstrates the feasibility of quantum-based frequent itemset mining

Provides a new approximation technique for quantum algorithms

Links data mining with quantum maximum clique problem

Abstract

In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.