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Michinori Nakata

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2020 – today

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2024
[c83]
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak, Junzo Watada:
Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules. IJCRS (1) 2024: 120-133
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Michinori Nakata, Hiroshi Sakai, Takeshi Fujiwara:
Dealing with Missing Values Meaning Unknown in Probabilistic Approximations. IJCRS (1) 2024: 157-172
2023
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Hiroshi Sakai, Michinori Nakata:
Descriptor-Based Information Systems and Rule Learning from Different Types of Data Sets with Uncertainty. IUKM (2) 2023: 255-266
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
Extending Kryszkiewicz's Formula of Missing Values in Terms of Lipski's Approach. IUKM (2) 2023: 294-305
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
Kryszkiewicz's Relation for Indiscernibility of Objects in Data Tables Containing Missing Values. IJCRS 2023: 170-184
2022
[j22]
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
Rough Sets and Rule Induction by an Approach Based on Coverings in Information Tables. J. Multiple Valued Log. Soft Comput. 39(5-6): 463-484 (2022)
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
The Lattice Structure of Coverings in an Incomplete Information Table with Value Similarity. IUKM 2022: 16-28
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
Structures Derived from Possible Tables in an Incomplete Information Table. SCIS/ISIS 2022: 1-6
[c76]
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Hiroshi Sakai, Michinori Nakata:
Apriori-based Rule Generation with Three-way Decisions for Heterogeneous and Uncertain Data. SCIS/ISIS 2022: 1-6
2021
[c75]
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Michinori Nakata, Norio Saito, Hiroshi Sakai, Takeshi Fujiwara:
Possible Coverings in Incomplete Information Tables with Similarity of Values. IJCRS 2021: 83-89
2020
[j21]
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Hiroshi Sakai, Michinori Nakata, Junzo Watada:
NIS-Apriori-based rule generation with three-way decisions and its application system in SQL. Inf. Sci. 507: 755-771 (2020)
[c74]
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Zhiwen Jian, Hiroshi Sakai, Takuya Ohwa, Kao-Yi Shen, Michinori Nakata:
An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions. IJCRS 2020: 95-110
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Rafael Bello, Duoqian Miao, Rafael Falcon, Michinori Nakata, Alejandro Rosete, Davide Ciucci:
Rough Sets - International Joint Conference, IJCRS 2020, Havana, Cuba, June 29 - July 3, 2020, Proceedings. Lecture Notes in Computer Science 12179, Springer 2020, ISBN 978-3-030-52704-4 [contents]

2010 – 2019

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2019
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Hiroshi Sakai, Michinori Nakata, Wei-Zhi Wu, Duoqian Miao, Guoyin Wang:
Guest Editorial: Rough Sets and Data Mining. CAAI Trans. Intell. Technol. 4(4): 201-202 (2019)
[j19]
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Hiroshi Sakai, Michinori Nakata:
Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination. CAAI Trans. Intell. Technol. 4(4): 203-213 (2019)
[j18]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rule induction based on rough sets from information tables having continuous domains. CAAI Trans. Intell. Technol. 4(4): 237-244 (2019)
[c73]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rough Sets Based on Possibly Indiscernible Classes in Incomplete Information Tables with Continuous Values. AISI 2019: 13-23
[c72]
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Michinori Nakata, Hiroshi Sakai:
Rule Induction Based on Rough Sets from Possibilistic Data Tables. IUKM 2019: 86-97
[c71]
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Hiroshi Sakai, Kao-Yi Shen, Michinori Nakata:
NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances. IUKM 2019: 247-259
[c70]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rough Sets Based on Possible Indiscernibility Relations in Incomplete Information Tables with Continuous Values. IJCRS 2019: 155-165
2018
[j17]
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Hiroshi Sakai, Kao-Yi Shen, Michinori Nakata:
On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL. J. Adv. Comput. Intell. Intell. Informatics 22(3): 394-403 (2018)
[c69]
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Hiroshi Sakai, Michinori Nakata, Junzo Watada:
A Model of Rule Generation Handling Granules Defined by Implications in Table Data Sets. ICDM Workshops 2018: 446-453
[c68]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rules Induced from Rough Sets in Information Tables with Continuous Values. IPMU (2) 2018: 490-502
[c67]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rule Induction Based on Indiscernible Classes from Rough Sets in Information Tables with Continuous Values. IJCSR 2018: 323-336
2017
[c66]
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Michinori Nakata, Hiroshi Sakai, Keitarou Hara:
Rough Sets in Incomplete Information Systems with Order Relations Under Lipski's Approach. IJCRS (1) 2017: 487-506
[c65]
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Hiroshi Sakai, Michinori Nakata, Junzo Watada:
A Proposal of Machine Learning by Rule Generation from Tables with Non-deterministic Information and Its Prototype System. IJCRS (1) 2017: 535-551
2016
[c64]
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Shusaku Tsumoto, Michinori Nakata, Hiroshi Sakai, Chenxi Liu:
A proposal of a privacy-preserving questionnaire by non-deterministic information and its analysis. IEEE BigData 2016: 1956-1965
[c63]
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Hiroshi Sakai, Chenxi Liu, Michinori Nakata:
Information Dilution: Granule-Based Information Hiding in Table Data - A Case of Lenses Data Set in UCI Machine Learning Repository. CMCSN 2016: 52-55
[c62]
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Michinori Nakata, Hiroshi Sakai:
Describing Rough Approximations by Indiscernibility Relations in Information Tables with Incomplete Information. IPMU (2) 2016: 355-366
[c61]
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Michinori Nakata, Hiroshi Sakai:
Rough Sets by Indiscernibility Relations in Data Sets Containing Possibilistic Information. IJCRS 2016: 187-196
[c60]
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Hiroshi Sakai, Chenxi Liu, Xiaoxin Zhu, Michinori Nakata:
On NIS-Apriori Based Data Mining in SQL. IJCRS 2016: 514-524
[c59]
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Chenxi Liu, Hiroshi Sakai, Xiaoxin Zhu, Michinori Nakata:
On Apriori-Based Rule Generation in SQL - A Case of the Deterministic Information System. SCIS&ISIS 2016: 178-182
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Michinori Nakata, Hiroshi Sakai:
Rough Approximations from Indiscernibility Relations under Incomplete Information. SCIS&ISIS 2016: 183-188
2015
[j16]
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Hiroshi Sakai, Mao Wu, Naoto Yamaguchi, Michinori Nakata:
Granules for association rules and decision support in the getRNIA system. Intell. Decis. Technol. 9(4): 309-320 (2015)
[c57]
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Hiroshi Sakai, Chenxi Liu, Michinori Nakata:
Families of the Granules for Association Rules and Their Properties. RSKT 2015: 175-187
2014
[j15]
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Hiroshi Sakai, Mao Wu, Michinori Nakata:
Apriori-Based Rule Generation in Incomplete Information Databases and Non-Deterministic Information Systems. Fundam. Informaticae 130(3): 343-376 (2014)
[j14]
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Mao Wu, Michinori Nakata, Hiroshi Sakai:
Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data. Int. J. Rough Sets Data Anal. 1(1): 32-47 (2014)
[j13]
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Naoto Yamaguchi, Mao Wu, Michinori Nakata, Hiroshi Sakai:
Application of Rough Set-Based Information Analysis to Questionnaire Data. J. Adv. Comput. Intell. Intell. Informatics 18(6): 953-961 (2014)
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Michinori Nakata, Hiroshi Sakai:
Rule induction based on rough sets from possibilistic information under Lipski's approach. GrC 2014: 218-223
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Hiroshi Sakai, Mao Wu, Naoto Yamaguchi, Chenxi Liu, Michinori Nakata:
Reconsideration of rules in tables with non-deterministic data. GrC 2014: 235-240
[c54]
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Michinori Nakata, Hiroshi Sakai:
An Approach Based on Rough Sets to Possibilistic Information. IPMU (3) 2014: 61-70
2013
[j12]
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Michinori Nakata, Hiroshi Sakai:
Twofold rough approximations under incomplete information. Int. J. Gen. Syst. 42(6): 546-571 (2013)
[j11]
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Hiroshi Sakai, Mao Wu, Michinori Nakata:
Division Charts as Granules and Their Merging Algorithm for Rule Generation in Nondeterministic Data. Int. J. Intell. Syst. 28(9): 865-882 (2013)
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Michinori Nakata, Hiroshi Sakai:
Rule induction based on rough sets from information tables containing possibilistic information. IFSA/NAFIPS 2013: 91-96
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Mao Wu, Michinori Nakata, Hiroshi Sakai:
An Overview of the getRNIA System for Non-deterministic Data. KES 2013: 615-622
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Hiroshi Sakai, Mao Wu, Naoto Yamaguchi, Michinori Nakata:
Rough Set-Based Information Dilution by Non-deterministic Information. RSFDGrC 2013: 55-66
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Hiroshi Sakai, Mao Wu, Naoto Yamaguchi, Michinori Nakata:
Non-deterministic Information in Rough Sets: A Survey and Perspective. RSKT 2013: 7-15
2012
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Hiroshi Sakai, Mao Wu, Michinori Nakata:
Association rule-based decision making in table data. Int. J. Reason. based Intell. Syst. 4(3): 162-170 (2012)
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Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Slezak:
Rough Sets-Based Machine Learning over Non-deterministic Data: A Brief Survey. AMLTA 2012: 3-12
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Mao Wu, Naoto Yamaguchi, Michinori Nakata, Hiroshi Sakai:
Learning a Table from a Table with Non-deterministic Information: A Perspective. AMLTA 2012: 24-32
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Hiroshi Sakai, Mao Wu, Naoto Yamaguchi, Michinori Nakata:
Division charts and their merging algorithm in Rough Non-deterministic information analysis. GrC 2012: 1-6
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
Management of Information Incompleteness in Rough Non-deterministic Information Analysis. IPMU (1) 2012: 280-289
2011
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Hiroshi Sakai, Hitomi Okuma, Michinori Nakata, Dominik Slezak:
Stable rule extraction and decision making in rough non-deterministic information analysis. Int. J. Hybrid Intell. Syst. 8(1): 41-57 (2011)
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Hitomi Okuma, Michinori Nakata, Dominik Slezak, Hiroshi Sakai:
Properties on inclusion relations and division charts in non-deterministic information systems. GrC 2011: 521-526
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Hiroshi Sakai, Michinori Nakata:
Toward association rules based decision making in Lipski's Incomplete Information Databases. GrC 2011: 570-575
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Michinori Nakata, Hiroshi Sakai:
Dual Rough Approximations in Information Tables with Missing Values. RSFDGrC 2011: 36-43
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
A Prototype System for Rule Generation in Lipski's Incomplete Information Databases. RSFDGrC 2011: 175-182
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
A NIS-Apriori Based Rule Generator in Prolog and Its Functionality for Table Data. RSKT 2011: 226-231
2010
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
The Lower and the Upper Systems of Rules in Tables with Missing Values. FGIT-DTA/BSBT 2010: 132-141
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Michinori Nakata, Hiroshi Sakai:
Two Rough Approximations for Information Tables Containing Missing Values. GrC 2010: 363-368
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
Toward Rough Sets Based Rule Generation from Tables with Uncertain Numerical Values. IUM 2010: 395-406
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Hitomi Okuma, Michinori Nakata, Dominik Slezak, Hiroshi Sakai:
An overview of decision making in Rough Non-deterministic Information Analysis. NaBIC 2010: 345-350
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Hiroshi Sakai, Michinori Nakata, Dominik Slezak:
Rule Generation in Lipski's Incomplete Information Databases. RSCTC 2010: 376-385

2000 – 2009

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2009
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Hiroshi Sakai, Hiroshi Kimura, Michinori Nakata:
An Overview of a Software Tool in Rough Non-deterministic Information Analysis. ISMVL 2009: 208-213
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Information Tables Containing Missing Values. ISMVL 2009: 286-291
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Hiroshi Sakai, Kohei Hayashi, Michinori Nakata, Dominik Slezak:
The Lower System, the Upper System and Rules with Stability Factor in Non-deterministic Information Systems. RSFDGrC 2009: 313-320
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Michinori Nakata, Hiroshi Sakai:
Rough Sets under Non-deterministic Information. RSKT 2009: 76-85
2008
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Hiroshi Sakai, Kazuhiro Koba, Michinori Nakata:
Rough Sets Based Rule Generation from Data with Categorical and Numerical Values. J. Adv. Comput. Intell. Intell. Informatics 12(5): 426-434 (2008)
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Information Tables Containing Possibilistic Values. Trans. Comput. Sci. 2: 180-204 (2008)
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Hiroshi Sakai, Ryuji Ishibashi, Kazuhiro Koba, Michinori Nakata:
Rules and Apriori Algorithm in Non-deterministic Information Systems. Trans. Rough Sets 9: 328-350 (2008)
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Michinori Nakata, Hiroshi Sakai:
Rough sets approximations in data tables containing missing values. FUZZ-IEEE 2008: 673-680
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Hiroshi Sakai, Ryuji Ishibashi, Michinori Nakata:
Lower and Upper Approximations of Rules in Non-deterministic Information Systems. RSCTC 2008: 299-309
2007
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Michinori Nakata, Hiroshi Sakai:
Lower and Upper Approximations in Data Tables Containing Possibilistic Information. Trans. Rough Sets 7: 170-189 (2007)
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Hiroshi Sakai, Kazuhiro Koba, Ryuji Ishibashi, Michinori Nakata:
On a Rough Sets Based Tool for Generating Rules from Data with Categorical and Numerical Values. MDAI 2007: 269-281
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Information Tables Containing Probabilistic Values. MDAI 2007: 282-294
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Data Tables Containing Missing Values. RSEISP 2007: 181-191
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Hiroshi Sakai, Ryuji Ishibashi, Kazuhiro Koba, Michinori Nakata:
On Possible Rules and Apriori Algorithm in Non-deterministic Information Systems: Part 2. RSFDGrC 2007: 280-288
2006
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Michinori Nakata:
Generalizing Possibility-Based Fuzzy Relational Models. J. Adv. Comput. Intell. Intell. Informatics 10(5): 633-646 (2006)
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Hiroshi Sakai, Michinori Nakata:
An Application of Discernibility Functions to Generating Minimal Rules in Non-Deterministic Information Systems. J. Adv. Comput. Intell. Intell. Informatics 10(5): 695-702 (2006)
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Michinori Nakata, Hiroshi Sakai:
Rough Sets Approximations to Possibilistic Information. FUZZ-IEEE 2006: 2343-2350
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Data Tables Containing Imprecise Information Under Probabilistic Interpretation. RSCTC 2006: 213-223
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Hiroshi Sakai, Michinori Nakata:
On Possible Rules and Apriori Algorithm in Non-deterministic Information Systems. RSCTC 2006: 264-273
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Michinori Nakata, Hiroshi Sakai:
Applying Rough Sets to Data Tables Containing Possibilistic Information. RSKT 2006: 147-155
2005
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Michinori Nakata, Hiroshi Sakai:
Rough-set-based approaches to data containing incomplete information: possibility-based cases. LAPTEC 2005: 234-241
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Hiroshi Sakai, Michinori Nakata:
Rough Sets Based Minimal Certain Rule Generation in Non-deterministic Information Systems: An Overview. LAPTEC 2005: 256-263
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Hiroshi Sakai, Tetsuya Murai, Michinori Nakata:
On a Tool for Rough Non-deterministic Information Analysis and Its Perspective for Handling Numerical Data. MDAI 2005: 203-214
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Michinori Nakata, Hiroshi Sakai:
Checking Whether or Not Rough-Set-Based Methods to Incomplete Data Satisfy a Correctness Criterion. MDAI 2005: 227-239
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Hiroshi Sakai, Michinori Nakata:
Discernibility Functions and Minimal Rules in Non-deterministic Information Systems. RSFDGrC (1) 2005: 254-264
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Michinori Nakata, Hiroshi Sakai:
Rough Sets Handling Missing Values Probabilistically Interpreted. RSFDGrC (1) 2005: 325-334
2004
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Michinori Nakata:
Considering Semantic Ambiguity and Indistinguishability for Values of Membership Attribute in Possibility-Based Fuzzy Relational Models. Rough Sets and Current Trends in Computing 2004: 159-168
2003
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Tetsuya Murai, Germano Resconi, Michinori Nakata, Yoshiharu Sato:
Granular Reasoning Using Zooming In & Out. RSFDGrC 2003: 421-424
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Tetsuya Murai, Yoshiharu Sato, Germano Resconi, Michinori Nakata:
Granular Reasoning Using Zooming In & Out: Aristotle's Categorical Syllogism. RSKD 2003: 186-197
2002
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Tetsuya Murai, Michinori Nakata, Yoshiharu Sato:
Association Rules and Non-Classical Logics. COMPSAC 2002: 1158-1163
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Michinori Nakata, Tetsuya Murai:
Functional Dependencies in Relational Expressions Based on Or-Sets. Rough Sets and Current Trends in Computing 2002: 161-166
2001
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Michinori Nakata, Tetsuya Murai:
Data Dependencies Over rough Relational Expressions. FUZZ-IEEE 2001: 1543-1546
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Tetsuya Murai, Michinori Nakata, Yoshiharu Sato:
A Note on Filtration and Granular Reasoning. JSAI Workshops 2001: 385-389
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Tetsuya Murai, Michinori Nakata, Yoshiharu Sato:
A Note on Conditional Logic and Association Rules. JSAI Workshops 2001: 390-394
2000
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Germano Resconi, Tetsuya Murai, Michinori Nakata, Masaru Shimbo:
Semantic field: introduction. KES 2000: 483-486
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Tetsuya Murai, Michinori Nakata, Yoshiharu Sato:
Generation of non-transitive chains from rational-valued and real-valued fuzzy subsets under rough set theory. KES 2000: 585-588
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Germano Resconi, Tetsuya Murai, Michinori Nakata:
Semantic field as a bridge between data and decision. KES 2000: 589-593
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Michinori Nakata, Tetsuya Murai, Germano Resconi:
Handling Data with Plural Sources in Databases. Rough Sets and Current Trends in Computing 2000: 404-411

1990 – 1999

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1999
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Michinori Nakata:
A Semantic-Ambiguity-Free Relational Model for Handling Imperfect Information¹. J. Adv. Comput. Intell. Intell. Informatics 3(1): 3-12 (1999)
1998
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Michinori Nakata, Germano Resconi, Tetsuya Murai:
On the treatment of imperfect information in intelligent databases. KES (3) 1998: 168-176
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Tetsuya Murai, Michinori Nakata, Masaru Shimbo:
Generation of accessible worlds under available information. KES (2) 1998: 236-242
1997
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Michinori Nakata, Germano Resconi, Tetsuya Murai:
Handling Imperfection in Databases: A Modal Logic Approach. DEXA 1997: 613-622
1996
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Michinori Nakata:
Unacceptable components in fuzzy relational databases. Int. J. Intell. Syst. 11(9): 633-647 (1996)

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