A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorith
Anxin Ye, Yongxian Jin · International Journal of Database Theory and Application · 2016
Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．