摘要
针对CBR系统中案例检索算法存在的问题,根据k-means算法思想,将案例库进行聚类,在聚类基础上设计了一个案例检索算法。分析了样本案例的选取规则,重点论述了案例检索算法。根据实验结果表明,该方法能够有效地提高案例检索结果的召回率及案例检索效率。
Aiming at the problems concerning case retrieval algorithm in the CBR system,this paper,in the light of the idea of the k-means algorithm,firstly clusteres the case database and then works out a case retrieval algorithm on the basis of the clustering of the case database.It analyzes the selecting principles of sample case,and mainly discusses the case retrieval algorithm.The results of experiment show that this algorithm can efficiently enhance the recall rate of the case retrieval outcomes and the efficiency of the case retrieval.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第4期185-187,共3页
Computer Engineering and Applications
关键词
基于案例推理
聚类
目标案例
相似度
case-based reasoning
clustering
target case
similarity
