Rough Sets Approximations to Possibilistic Information

Rough sets are applied to data tables containing possibilistic information. A family of weighted equivalence classes is obtained, in which each equivalence class is accompanied by a possibilistic degree to which it is an actual one. By using the family of weighted equivalence classes we can derive a lower approximation and an upper approximation. The lower approximation and the upper approximation coincide with those obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified.

[1]  Shashi K. Gadia,et al.  Incomplete Information in Relational Temporal Databases , 1992, VLDB.

[2]  Hiroshi Sakai,et al.  Rough-set-based approaches to data containing incomplete information: possibility-based cases , 2005, LAPTEC.

[3]  Jerzy W. Grzymala-Busse,et al.  On the Unknown Attribute Values in Learning from Examples , 1991, ISMIS.

[4]  Alain Pirotte,et al.  Imperfect Information in Relational Databases , 1996, Uncertainty Management in Information Systems.

[5]  Marzena Kryszkiewicz,et al.  Data mining in incomplete information systems from rough set perspective , 2000 .

[6]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[7]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[8]  Jerzy Stefanowski,et al.  Rough classification in incomplete information systems , 1989 .

[9]  Hiroshi Sakai Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information , 1998, Rough Sets and Current Trends in Computing.

[10]  Hiroshi Sakai,et al.  Rough Sets Handling Missing Values Probabilistically Interpreted , 2005, RSFDGrC.

[11]  Hiroshi Sakai,et al.  Applying Rough Sets to Data Tables Containing Imprecise Information Under Probabilistic Interpretation , 2006, RSCTC.

[12]  Simon Parsons,et al.  Addendum to "Current Approaches to Handling Imperfect Information in Data and Knowledge Bases" , 1996, IEEE Trans. Knowl. Data Eng..

[13]  Hiroshi Sakai,et al.  An Algorithm for Finding Equivalence Relations from Tables with Non-Deterministic Information , 1999, RSFDGrC.

[14]  Alexis Tsoukiàs,et al.  Incomplete Information Tables and Rough Classification , 2001, Comput. Intell..

[15]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..

[16]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[17]  Alexis Tsoukiàs,et al.  On the Extension of Rough Sets under Incomplete Information , 1999, RSFDGrC.

[18]  Hiroshi Sakai,et al.  Checking Whether or Not Rough-Set-Based Methods to Incomplete Data Satisfy a Correctness Criterion , 2005, MDAI.

[19]  Tomasz Imielinski,et al.  Incomplete Information in Relational Databases , 1984, JACM.

[20]  Salvatore Greco,et al.  Handling Missing Values in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems , 1999, RSFDGrC.