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

CAAI Transactions on Intelligence Technology

Hiroshi Sakai¹  Michinori Nakata² 

[1] Graduate School of Engineering, Kyushu Institute of Technology;Josai International University;

关键词: knowledge acquisition;    information analysis;    software tools;    rough set theory;    database management systems;    data mining;    information systems;    rule generators;    apriori-based rule generation;    table data sets;    computational methodologies;    information incompleteness;    granular computing;    association rules;    rough sets nondeterministic information analysis;    incomplete information databases;    nondeterministic information systems;    apriori algorithm;    outstanding researches;    data mining;    novel researches;    intelligent rule generator;    authors;   

DOI  :  10.1049/trit.2019.0001

来源: DOAJ

【 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, Orłowska'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.

【 授权许可】

Unknown