Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination

• 作者: Hiroshi Sakai Michinori Nakata
• 来源期刊: 智能技术学报
• 学科: 工学
• 关键词: APRIORI RULE INCOMPLETE
• 年，卷（期）: znjsxb_2019,（4）
• 页码范围: 203-213
• 页数: 11页
• 分类号: TP3

摘要

The authors have been coping with new computational methodologies such as rough sets,information incompleteness,data mining,granular computing,etc.,and developed some software tools on association rules as well as new mathematical frameworks.They simply term this research Rough sets Non-deterministic Information Analysis(RNIA).They followed several novel types of research,especially Pawlak’s rough sets,Lipski’s incomplete information databases,Or?owska’s non-deterministic information systems,Agrawal’s Apriori algorithm.These are outstanding researches related to information incompleteness,data mining,and rule generation.They have been trying to combine such novel researches,and they have been trying to realise more intelligent rule generator handling data sets with information incompleteness.This study surveys the authors’research highlights on rule generators,and considers a combination of them.