基于遗传算法的多维快速聚类算法研究

Research of Multi-dimensions and Speediness Clustering Based on Genetic Algorithm

引入遗传算法试图解决海量、高维样本的聚类问题。分析了目前基于样本和属性值两类基于遗传算法的聚类算法的不足,归纳出它们的算法模型。针对多维快速聚类问题提出了密度法、网格法两种基于遗传算法的聚类算法。算法测试表明,改进后的基于遗传算法的聚类方法能够解决海量、高维样本的聚类问题。

The genetic algorithm is introduced in order to solve the clustering about mass ,multi-dimensions samples.The shortcomings are analysised, which lie in sample and attribute value clustering algorithms based on the genetic algorithm at present, and their algorithm models are summarized. The density and girding algorithms based on the genetic algorithm are put forward to multi-dimensions and speediness clustering. Two algorithms trail show: the betterment clustering algorithms based on genetic algorithm can accomplish preferably the clustering about mass ,multi-dimensions samples.