Rough set theory, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. The main idea is based on the indiscernibility relation that describes indistinguishability of objects. Concepts are represented by lower and upper approximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, data mining, machine learning, finance, industry, multimedia, medicine, and most recently bioinformatics.
RSFDGrC 2005 is a continuation of international conferences and workshops devoted to the subject of rough sets, held alternatively in Canada, China, Japan, Poland, Sweden, and the USA. RSFDGrC achieved the status of bi-annual international conference, starting from the year of 2003, in Chongqing, China.
RSFDGrC 2005 encompasses rough sets and fuzzy sets, granular computing, as well as knowledge discovery and data mining. We also welcome submissions addressing the connections of the main conference scopes to the following areas:
We plan special sessions on applications to bioinformatics, medicine, industry, and environmental problems. We welcome any other proposals for special sessions as well.