基于区间属性值补缺的知识管理自学习案例检索机制

针对知识管理自学习案例检索中存在区间属性以及属性值缺失的情况,提出了基于区间属性值补缺的案例检索机制.首先,运用粗集方法对知识管理自学习案例进行分类,完成初步检索并在分类基础上插补缺失的属性值;而后,利用客观性较强的熵权法完成对确定值和区间属性的权重配置;最后,应用区间灰关联方法实现对知识管理自学习案例的检索功能.算例分析表明,所提出的案例检索方法操作简便、合理有效.

Knowledge Management Self-learning Case Retrieval Mechanism Based on Filling Missing Interval-valued Attribute

According to the problems of missing attribute values and interval-valued attributes in KM self- learning case retrieval, a case retrieval mechanism based on filling missing interval-valued attribute was put forward. Firstly, rough set theory was used to classify cases, and complete the preliminary retrieval. The missing attribute values were filled up on this basis. Then the objective method of entropy weighting was applied to calculate the weight of certainties and interval attributes. Finally, the grey interval rele- vancy theory was introduced to realize the function of KM self-learning case retrieval. In the end, the case analysis showed the proposed retrieval method was simple and effective.