摘要
围绕知识管理和提高数据挖掘模型的可解释性问题展开研究,提出了采用协同挖掘的方法对同源数据进行模式评估和知识管理的CMA算法(Collaborative Mining Algorithm)。与集成学习产生同一类型知识规则的组合学习方式不同,协同挖掘在同源数据的基础上建立不同类型的学习模型,并且每类学习模型产生的知识规则的表现形式各不相同,通过比对学习形成了一致的知识规则。实验表明,协同挖掘可以有效发现数据中的隐含信息,提高知识管理的性能。
This article explored the issues of knowledge management and improvement of the interpretability of data mining models,and proposed the collaborative mining algorithm （CMA）,which performs pattern evaluation and knowledge management based on collaborative mining of homologous data.In contrast to the ensemble learning knowledge rules by combining learning models of the same type,collaborative mining sets up learning models of different types based on homologous data,and each model owns different forms of knowledge rules.Through the comparison study,coincident knowledge rules were formed.Experiments show that collaborative mining can efficiently find the latent information in data,and improve the performance of knowledge management.
出处
《计算机科学》
CSCD
北大核心
2014年第12期143-147,共5页
Computer Science
基金
国家自然科学基金(61371155)资助
关键词
同源数据
协同挖掘
模型评估
知识管理
Homologous data
Collaborative mining
Model evaluation
Knowledge management
