基于属性的概念格快速渐进式构造算法优化

Attribute-Based Concept Lattice Fast Incremental Construction Algorithm Optimization

文章提出一种基于属性的概念格快速渐进式构造算法,主要解决传统算法存在执行时间长和准确率低等问题。首先,该算法引入单属性权值、多属性权值、阈值设定3个内容,并对其进行优化。其次,利用信息熵和偏好修正系数,对单属性权值获取进行调整,从原始数据中获取多属性权值。最后,通过阈值设定衡量多属性的重要性,同时约束条件获取合理的属性权值取值结果。实验结果表明,该算法优化后生成的概念数量小于原概念格中概念的总数,执行时间更短,准确率更高。

This article proposes an attribute-based concept lattice fast incremental algorithm,which aims to solve the problems of long execution time and low accuracy in traditional algorithms.Firstly,the algorithm introduces three contents:single attribute weight,multiple attribute weight,and threshold setting,and optimizes them.Secondly,information entropy and preference correction coefficient are used to adjust the acquisition of single attribute weights,and multiple attribute weights are obtained from the original data.Finally,the importance of multiple attributes is measured through threshold setting,and reasonable attribute weight values are obtained through constraint conditions.The experimental results show that the optimized algorithm generates fewer concepts than the total number in the original concept lattice,with shorter execution time and higher accuracy.