K-Means Clustering Analysis Based on Genetic Algorithm

Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.To solve such problems,this paper presents an improved K-Means algorithm based on genetic algorithm.It combines the locally searching capability of the K-Means with the global optimization capability of genetic algorithm,and introduces the K-Means operation into the genetic algorithm of adaptive crossover probability and adaptive mutation probability,which overcomes the sensitivity to the initial start centers and locality of K-Means.Experimental results demonstrate that the algorithm has greater global searching capability and can get better clustering.