遗传优化的K均值聚类算法

Optimized K-Means Clustering Analysis Based on Genetic Algorithm

在K均值聚类算法中,K值需事先确定且在整个聚类过程中不能改变其大小,而按照经验K值划分所得的最终聚类结果一般并非最佳结果。通过求解所构造适应度函数的值,在变异操作中实现最佳聚类数K值的自动寻优,同时借助遗传操作完成聚类中心点的优化选取并利用遗传算法的全局寻优能力克服了K均值聚类算法的局部性。通过对Iris等数据集的实验分析,证明该算法具有良好的全局收敛性,且通过K值的自动调整,有效提高了聚类结果的划分。

For K-Means clustering algorithm, the k value must be determined in advance and can＇t be changed. However, the value is usually not the best if it is determined by experience. In this paper, fitness is taken into account to look for optimal number automatically in the mutation operations. Also, genetic operation is used to select the centers accordingly. In addition, the global optimization capability of genetic algorithm can overcome the locality of K-Means clustering algorithm. The experimental results show that this algorithm has better global searching capability and can efficiently improve the clustering result by adjusting the k value automatically.