基于基因表达式编程的K均值自动聚类算法

K-means Auto-Clustering Algorithm Based on Gene Expression Programming

研究数据挖掘,优化聚类循序算法,针对提高分类的效率和准确性难题,传统聚类算法不能自动聚类的问题,为提高聚类算法的收敛速度和寻优精度,提出了一种改进的自动聚类算法,从分析基因表达式编程和k均值算法的特点出发,融合了两种算法优点,实现了在未知簇划分信息的情况下对数据集自动进行聚类分析。实践中选择k-均值算法和改进算法对聚类进行了仿真检验。结果表明,将改进的自动聚类算法应用于G IS物流选址优化中,与传统聚类算法相比,改进的算法不仅具有比较快的收敛速度和聚类精度,而且使得聚类结果更有参考价值。

In order to solve the problem that the traditional clustering algorithm can not automatically clustering,and to improve the convergence speed and optimization accuracy of clustering algorithm,an improved automatic clustering algorithm is proposed.This algorithm combines with characteristics of K-means algorithm and Gene Expression Programming,and achieves automatically cluster analysis for data sets in the case of unknown clusters information.K-means algorithm and the improved algorithm are selected to examine clustering capacity.The improved Auto-Clustering algorithm is used in GIS selection optimization of logistics by the simulation.The results showed that compared with traditional clustering algorithms,the improved algorithm not only has faster convergence speed and the clustering accuracy,but also makes the clustering results more reference value.