东南大学学报（自然科学版）2016，Vol.18Issue(3)：464-469,6.DOI:10.3969/j.issn.1001-0505.2016.03.002
面向生产调度规则挖掘的关键属性提取技术
Attribute extraction for rule discovery of production scheduling
焦磊 1刘晓军 2刘庭煜 1倪中华2
作者信息
- 1. 东南大学机械工程学院，南京 211189
- 2. 东南大学江苏省微纳生物医疗器械设计与制造重点实验室，南京 211189
- 折叠
摘要
Abstract
An algorithm for attribute extraction is proposed to meet the objective demand of produc-tion scheduling rule discovery for data set attribute reduction.Firstly,the characteristics of the pro-duction data are analyzed,and the attributes of production data are divided into several sets according to their importance and correlation.Then,the importance objective function is established to find the important attributes by using the fuzzy entropy and the clustering accuracy.Finally,the correlation analysis is used to find the related attributes of the important attribute,which are then merged to form the important composite attribute to enhance the effect of attribute extraction.In order to verify the validity of the technology,a subset obtained by the technique is compared with another subset ob-tained by the stochastic method,and the compatibility and the accuracy of rule extraction between them are analyzed.The experimental results show that the data subset formed by attribute extraction has lower incompatibility and can concentrate the scheduling rule knowledge of the original data sets, which mean that the accuracy and efficiency of a variety of scheduling rule discovery algorithms can be improved significantly.Thus,the technology developed is suitable for the attribute extraction in the preprocessing stage of the production scheduling rule discovery.关键词
数据挖掘/属性提取/模糊数学/模糊熵Key words
data mining/attribute extraction/fuzzy math/fuzzy entropy分类
信息技术与安全科学引用本文复制引用
焦磊,刘晓军,刘庭煜,倪中华..面向生产调度规则挖掘的关键属性提取技术[J].东南大学学报（自然科学版）,2016,18(3):464-469,6.
