Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination
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-determ...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0001 |
| _version_ | 1818439653793988608 |
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| author | Hiroshi Sakai Michinori Nakata |
| author_facet | Hiroshi Sakai Michinori Nakata |
| author_sort | Hiroshi Sakai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T17:59:53Z |
| format | Article |
| id | doaj.art-bbaa6b4fa13b4f9688cde36a71e7f4e6 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-12-14T17:59:53Z |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-bbaa6b4fa13b4f9688cde36a71e7f4e62022-12-21T22:52:28ZengWileyCAAI Transactions on Intelligence Technology2468-23222019-06-0110.1049/trit.2019.0001TRIT.2019.0001Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combinationHiroshi Sakai0Michinori Nakata1Graduate School of Engineering, Kyushu Institute of TechnologyJosai International UniversityThe 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.https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0001knowledge acquisitioninformation analysissoftware toolsrough set theorydatabase management systemsdata mininginformation systemsrule generatorsapriori-based rule generationtable data setscomputational methodologiesinformation incompletenessgranular computingassociation rulesrough sets nondeterministic information analysisincomplete information databasesnondeterministic information systemsapriori algorithmoutstanding researchesdata miningnovel researchesintelligent rule generatorauthors |
| spellingShingle | Hiroshi Sakai Michinori Nakata Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination CAAI Transactions on Intelligence Technology 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 |
| title | Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination |
| title_full | Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination |
| title_fullStr | Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination |
| title_full_unstemmed | Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination |
| title_short | Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination |
| title_sort | rough set based rule generation and apriori based rule generation from table data sets a survey and a combination |
| topic | 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 |
| url | https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0001 |
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