Abstract: 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.

摘要： 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。