A Novel Clustering Algorithm Based on Quantum Games

TL;DR

This paper introduces a quantum-game-based clustering algorithm where data points are modeled as players in quantum games, leading to efficient and effective clustering demonstrated through simulations and comparisons.

Contribution

It develops a novel quantum game framework for data clustering, integrating quantum strategies and link adjustments to improve clustering performance.

Findings

Data points are clustered reasonably and efficiently.

The algorithm converges quickly.

Comparison shows effectiveness over other methods.

Abstract

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.