Based on the concepts and principles of quantum computing

a novel quantum-inspired evolutionary algorithm for data clustering (QEAM) is proposed in this paper by using a novel distance measurement index called manifold distance which can measure the geodesic distance along with the manifold.The clustering problem is viewed as an optimization problem.Our main motives of using QEAM consist in searching for appropriate cluster center by using the principles of quantum evolutionary computation

so that a similarity metric of clusters are optimized more quickly and effectively.The experimental results on six artificial datasets and three UCI datasets show the superiority of QEAM over an immune evolutionary clustering algorithm with manifold distance (IEAM)

a genetic algorithm for clustering (GAC) and fuzzy c-means algorithm (FCM).