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2. Rule induction based on rough sets from information tables having continuous domains

Author

Michinori Nakata, Hiroshi Sakai, and Keitarou Hara

Subjects
approximation theory, rough set theory, rule induction, continuous domains, neighbourhood rough sets, complete information tables, lower approximation, possible lower ones, upper approximation, possible upper ones, extended approximations, possible world semantics, possible indiscernibility relations, single rule, consistent rules, inconsistent rules, combined rule, incomplete information tables, possible ones, consistent ones, possible lower approximations, inconsistent ones, possible upper approximations, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765
Abstract

Information tables having continuous domains are handled by neighborhood rough sets. Two approximations in complete information tables are extended to handle incomplete information. Consequently, four approximations are obtained: certain and possible lower ones and certain and possible upper ones without computational complexity. These extended approximations create the same results as the ones from possible world semantics by using possible indiscernibility relations. Therefore, the extension is justified. In complete information tables two types of single rules that an object supports are obtained: consistent and inconsistent ones. The single rule has low applicability. To increase applicability, a series of single rules are brought into one combined rule with an interval value. In incomplete information tables four kinds of single rules are obtained. From them, four kinds of combined rules are obtained: certain and consistent, possible and consistent, certain and inconsistent, and possible inconsistent ones. A combined rule has higher applicability than the single rules from which it is assembled.

Published
2019
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3. Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination

Author

Hiroshi Sakai and Michinori Nakata

Subjects
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, novel researches, intelligent rule generator, authors, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765
Abstract

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.

Published
2019
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