基于概念格和条件信息熵的分类规则获取方法

Classification rule acquisition method based on concept lattice and conditional entropy

分类规则挖掘是数据挖掘中的重要研究内容之一,概念格是提取分类规则的一种有效工具。首先,给出了一种面向分类的概念格批处理构造算法CLBCR,并从概念格内涵中提取分类规则;其次,采用条件信息熵作为分类规则的度量因子,对分类规则进行排序,从而进一步提高了分类规则的分类效率;最后,实验验证了该方法,在不影响分类正确率的同时,有效地提高了分类效率。

Classification rule mining is an important task in data mining,and concept lattice is an effective tool for mining classification rules.Firstly,a batch constructing algorithm of concept lattice oriented classification is presented and classification rule set is extracted from the intent of the concept lattice.Secondly,the rule order in the classification rule set is arranged by using conditional entropy as a measurement factor so that the classification efficiency is enhanced.In the end,the experimental results show the algorithm can effectively improve the classification efficiency under the classification accuracy unchanged.