On a tool for rough non-deterministic information analysis and its perspective for handling numerical data

Authors:
Hiroshi Sakai;Tetsuya Murai;Michinori Nakata
Affiliations:
Department of Mathematics and Computer Aided Science, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, Japan;Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Kita-ku, Sapporo, Japan;Faculty of Management and Information Science, Josai International University, Togane, Chiba, Japan
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

Citing 11
Cited 1

Variable precision rough set model

Journal of Computer and System Sciences
A new version of the rule induction system LERS

Fundamenta Informaticae
On semantic issues connected with incomplete information databases

ACM Transactions on Database Systems (TODS)
On Databases with Incomplete Information

Journal of the ACM (JACM)
Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic

Information Sciences—Informatics and Computer Science: An International Journal
A relational model of data for large shared data banks

Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems

Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
An Algorithm for Finding Equivalence Relations from Tables with Non-Deterministic Information

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Effective Procedures for Handling Possible Equivalence Relations in Non-deterministic Information Systems

Fundamenta Informaticae
Granular reasoning using zooming in & out: part 1. propositional reasoning

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

On a Rough Sets Based Tool for Generating Rules from Data with Categorical and Numerical Values

MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

RoughNon-deterministicInformationAnalysis (RNIA) is a framework for handling rough sets based concepts, which are defined in not only DeterministicInformationSystems (DISs) but also Non-deterministicInformationSystems (NISs), on computers. This paper at first reports an overview of a tool for RNIA. Then, we enhance the framework of RNIA for handling numerical data. Most of DISs and NISs implicitly consist of categorical data, and multivariate analysis seems to be employed for numerical data. Therefore, it is necessary to investigate rough sets based information analysis for numerical data, too. We introduce numerical patterns into numerical values, and define equivalence relations based on these patterns. Due to this introduction, it is possible to handle the precision of information, namely it is possible to define fine information and coarse information. These fine and coarse concepts cause more flexible information analysis, including rule generation, from numerical data.