计算机应用研究2018，Vol.35Issue(4)：988-991,1027,5.DOI:10.3969/j.issn.1001-3695.2018.04.006
基于混合频繁模式树的粗糙集属性约减算法的研究与应用
Research and application on rough set attribute reduction model based on mixed frequent pattern tree
林春喜 1徐宏喆 1王谊青 2李文1
作者信息
- 1. 西安交通大学电子信息工程学院,西安710049
- 2. 中山大学数据科学与计算机学院,广州510006
- 折叠
摘要
Abstract
Rough set theory has essential meaning in theory and practical value for the attribute reduction model of learning analysis system.In order to solve the problem of the high dimension,incompleteness and incremental property of education big data,this paper proposed a discernibility information algorithm and an incremental updating algorithm based on incomplete decision table.Then,it combined the tree structure for effective storage of discernibility information and the core concept of rough set to design and construct the MIX_FP tree (mixed frequent pattern tree) structure,which could provide effective reduction of high dimension attribute of information system decision table.The experiment results show that the rough set attribute reduction algorithm based on MIX_FP tree has good operational efficiency and spatial performance.The model provides an effective support for the attribute reduction of education data,and provides a new research idea for the research and application on learning analysis field of attribute reduction algorithm based on rough set theory.关键词
属性约减/粗糙集/差别信息/MIX_FP树/学习分析技术Key words
attribute reduction/rough set/discernibility information/MIX_FP tree/learning analysis technique分类
信息技术与安全科学引用本文复制引用
林春喜,徐宏喆,王谊青,李文..基于混合频繁模式树的粗糙集属性约减算法的研究与应用[J].计算机应用研究,2018,35(4):988-991,1027,5.
