Dual rough approximations in information tables with missing values

Authors:
Michinori Nakata;Hiroshi Sakai
Affiliations:
Faculty of Management and Information Science, Josai International University, Togane, Chiba, Japan;Department of Mathematics and Computer Aided Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, Japan
Venue:
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Year:
2011

Citing 16
Cited 0

Incomplete Information in Relational Databases

Journal of the ACM (JACM)
The structure of the relational database model

The structure of the relational database model
Rules in incomplete information systems

Information Sciences: an International Journal
On semantic issues connected with incomplete information databases

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

Journal of the ACM (JACM)
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Problem of Incomplete Information in Relational Databases

Problem of Incomplete Information in Relational Databases
Foundations of Databases: The Logical Level

Foundations of Databases: The Logical Level
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases

IEEE Transactions on Knowledge and Data Engineering
Handling Missing Values in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Maximal consistent block technique for rule acquisition in incomplete information systems

Information Sciences: an International Journal
Effective Procedures for Handling Possible Equivalence Relations in Non-deterministic Information Systems

Fundamenta Informaticae
Applying Rough Sets to Information Tables Containing Probabilistic Values

MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Rough sets handling missing values probabilistically interpreted

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Checking whether or not rough-set-based methods to incomplete data satisfy a correctness criterion

MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Set-valued information systems

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A method of possible equivalence classes has been developed under information tables with missing values. To deal with imprecision of rough approximations that comes from missing values, the concepts of certainty and possibility are used. When an information table contains missing values, two rough approximations, certain and possible ones, are obtained. The actual rough approximation lies between the certain and possible rough approximations. The method gives the same results as a method of possible worlds. This justifies the method of possible equivalence classes. Furthermore, the method is free from the restriction that missing values may occur to only some specified attributes. Hence, we can use the method of possible equivalence classes to obtain rough approximations between arbitrary sets of attributes having missing values.