摘要
事例检索是基于事例推理CBR系统的中心环节,检索速度和精度关系着整个系统的质量。根据应用的需要先后分析了基于数据库、ID3决策树和k d树的事例检索算法,并在标准的k d树算法的基础上引入聚类的概念,将原始的事例库组织成聚合中心库和类库二级结构,提出了改进的k d树事例检索算法,理论分析和对比实验证实了改进的k d树事例检索算法在事例检索速度上逼近ID3决策树检索算法,在检索精度上远远超过了ID3决策树检索算法和标准的k d树检索算法,其性能开销比是四种算法中最为理想的。
Case retrieval is the focus of the CaseBased Reasoning system, and it determines the speed and accuracy of the whole system. First, We compare the algorithms based on SQL, ID3 and standard kd tree. Then, we introduce clustering into standard kd tree, so we can divide case base into a twolevel structure, clusteringcenter base and class base, to establish an advanced kd tree. The analyses and experiments have proved that the retrieving speed of our approach nears ID3s, and its accuracy is much better than others.
出处
《计算机应用》
CSCD
北大核心
2003年第6期13-16,共4页
journal of Computer Applications