自适应数据库中基于特征向量的聚类算法的研究与改进

在负载自适应数据库系统中，负载特征化部件是关键部分，首先要对负载分类，然后根据分类的情况预测负载性能。负载的分类一般采用聚类算法，聚类算法中比较典型的就是K—means算法。但在K—means算法中，k值必须提前设定而且不能根据负载的实际情况改变，就是对算法的一个改进，使得k值动态的、能够根据负载的实际情况改变。实验结果表明，使用该算法的分类结果预测负载运行时间的准确性有明显提高。

The Research and Improvement of the Clustering Algorithm based on Feature Vectors in Autonomic Database

In autonomic database system,workload characterization is a key part.In workload characterization,workload should be class,then anticipate workload performance.Workload classification use clustering algorithm,and in clustering algorithm,the typical is the K-means algorithm.But in the K-means clustering algorithm,k should be defined and not changed.This paper makes a improvement in the algorithm,so the k is changed if needed.The result of the experiment shows that the veracity used cluster based on feature vectors makes of forecast workload runtime is improved.