Research and Improvement on K-Means Clustering Algorithm
Xuemei Wang, Jinbo Wang · 2012
According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution.A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with kmeans clustering algorithm at the same time.The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability，and more effectively improved clustering quality and convergence speed.