自适应数据库中基于特征向量的聚类算法

Research of cluster based on feature vectors in autonomic database

负载自适应数据库系统中,负载特征化部件要实时对各种数据库的访问负载分类,根据分类的情况预测负载对数据库资源需求。是对常规聚类算法的一个改进,提出基于特征向量的聚类算法和基于特征向量的增量聚类算法。使用该算法后负载分类速度和准确性有明显提高。

In autonomic database system,workload characterization is a key part.In workload characterization,workload should be classified,then anticipate workload performance.Workload classification uses cluster algorithm.And in cluster algorithm,the typical is the K-means cluster algorithm.But in the K-means cluster algorithm,k should be defined and not changed.This paper makes an improvement in the algorithm,so the k is changed if needed.The result of the experiment shows that the veracity using Cluster algorithm Based on Feature Vectors（CFV） making of forecasting workload runtime is improved.