Simulation and application on data clustering based on an improved FCM algorithm

Abstract

An improved data clustering algorithm was proposed based on the Fuzzy C-Means (FCM) algorithm for the purpose of clustering the data precisely and effectively, through progressing the performance of the data clustering to afford the element work for the application of fault diagnosis and target recognition and so on. There was fatal weakness for the traditional FCM algorithm that the algorithm is sensitive to initial value and noise. The chaotic differential evolution FCM algorithm was proposed according to the efficient global search capability of differential evolution algorithm and the traversal characteristic of chaotic time series. The improved algorithm used the Logistics chaotic mapping to search for the optimal solution, and the chaos disturbance was introduced into the evolutionary population to make up for the defects of FCM algorithm. The new method can overcome the problems of initial value sensitiveness with FCM and local convergence with genetic algorithm. Because the new method. The three types of typical vibration data of faults engines was taken as the example for the research and application. The simulation and application result shows that the data clustering performance of the improved FCM algorithm is much better than the traditional FCM algorithm, and the accuracy rates of fault diagnosis in the application was increased by more than twenty percent, it shows good application prospect.