The Rough Set Database System: An Overview

The paper describes the “Rough Sets Database System” (called in short the RSDS system) for the creation of a bibliography on rough sets and their applications. This database is the most comprehensive online rough sets bibliography currently available and is accessible from the RSDS website at http://www.rsds.wsiz.rzeszow.pl. This service has been developed to facilitate the creation of a rough sets bibliography for various types of publications. At the moment the bibliography contains over 1900 entries from more than 815 authors. It is possible to create the bibliography in HTML or BibTeX format. In order to broaden the service contents it is possible to append new data using a specially dedicated online form. After appending data online the database is updated automatically. If one prefers sending a data file to the database administrator, please be aware that the database is updated once a month. In the present version of the RSDS system, we have broadened information about the authors as well as the Statistics sections, which facilitates precise statistical analysis of the service. In order to widen the abilities of the RSDS system we added new features including:Detailed information concerning the software connected with the rough sets methodology. Scientific biographies of the outstanding people who work on rough sets.

[1]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[2]  Wojciech Ziarko,et al.  Rough Sets and Knowledge Discovery: An Overview , 1993, RSKD.

[3]  J. Kacprzyk,et al.  Incomplete Information: Rough Set Analysis , 1997 .

[4]  Tsau Young Lin Introduction to the special issue on rough sets , 1996, Int. J. Approx. Reason..

[5]  Tsau Young Lin,et al.  Rough Set Methods and Applications , 2000 .

[6]  Andrzej Skowron,et al.  Rough-Neural Computing: Techniques for Computing with Words , 2004, Cognitive Technologies.

[7]  Yiyu Yao,et al.  Rough Sets and Current Trends in Computing : second International Conference, RSCTC 2000, Banff, Canada, October 16-19, 2000 : revised papers , 2001 .

[8]  Andrzej Skowron,et al.  Applications, case studies and software systems , 1998 .

[9]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[10]  Stéphane Demri,et al.  Incomplete Information: Structure, Inference, Complexity , 2002, Monographs in Theoretical Computer Science An EATCS Series.

[11]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[12]  Andrzej Skowron,et al.  Rough-Neural Computing , 2004, Cognitive Technologies.

[13]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[14]  Tsau Young Lin Workshop on rough sets and database mining (panel) , 1995, CSC '95.

[15]  Ian Witten,et al.  Data Mining , 2000 .

[16]  R. Słowiński Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[17]  Andrzej Skowron,et al.  Rough Sets , 1995, Lecture Notes in Computer Science.

[18]  A. Skowron,et al.  Methodology and applications , 1998 .

[19]  Witold Pedrycz,et al.  Computational intelligence in software engineering , 1997, CCECE '97. Canadian Conference on Electrical and Computer Engineering. Engineering Innovation: Voyage of Discovery. Conference Proceedings.

[20]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[21]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.

[22]  A. Skowron,et al.  Rough sets and current trends in computing : Third International Conference, RSCTC 2002, Malvern, PA, USA, October 14-16, 2002 : proceedings , 2002 .

[23]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[24]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[25]  Wojciech Ziarko,et al.  INTRODUCTION TO THE SPECIAL ISSUE ON ROUGH SETS AND KNOWLEDGE DISCOVERY , 1995, Comput. Intell..

[26]  S. Tsumoto,et al.  Rough set methods and applications: new developments in knowledge discovery in information systems , 2000 .

[27]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[28]  S. Tsumoto,et al.  Rough Set Theory and Granular Computing , 2003 .