用遗传算法优化初始聚类中心的K-means算法研究

K-means algorithm for optimizing initial clustering centers by genetic algorithm

传统的K-means算法是经典的数据挖掘算法之一,收敛速度快,但是初始聚类中心K的选取聚有随机性,影响到聚类效果,因此本采用遗传算法去对初始值k的选取优化,实验证明该用遗传算法优化实始聚类中心的Kmeans算法比传统K-means算法聚类效果更好。

The traditional K-means algorithm is one of the classical data mining algorithms with fast convergence speed,but the selection of the initial clustering center K is random,which affects the clustering effect.Therefore,the genetic algorithm is used to optimize the selection of the initial value K.Experiments show that the K-means algorithm using genetic algorithm to optimize the real clustering center has better clustering effect than the traditional K-means algorithm.