Rough sets was developed by Zdzislaw Pawlak in the early 1980's.
The main goal of the rough set analysis is induction of approximations
of concepts. Rough sets constitutes a sound basis for data mining
and It offers mathematical tools to discover patterns hidden in data.
Rough sets can be used for feature selection, feature extraction,
data reduction, decision rule generation, and pattern extraction
(templates, association rules) etc.
In this talk, the core ideas of rough sets are shown.
Then, the computation of reducts, which is mostly used in
data mining context, is introduced. Finally, the extensions
of original rough set theory and the future research direction
will be shown.
Bio:
Shusaku Tsumoto received his M.D. from Osaka University, School of Medicine
in 1989 and received his Ph.D. from Tokyo Institute of Technology in 1997.
He is a Professor at Department of Medical Informatics, Shimane Medical
University from 2000. He won the best paper award in 1993 and 1995 from
the Japanese Association of Medical Informatics. His interests include
neurology, approximate reasoning, data mining, fuzzy sets, non classical logic,
knowledge acquisition, Petri nets, and rough sets (alphabetical order).
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