A Talk on Rough Sets on Monday
Shampa Chakraverty
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DATE & VEVUE: 11/2/2013, Main Audi
TIME: 10:00 a.m. sharp
TITLE:
“Rough Sets – Modern Applications & Scalability Challenges”
By Prof Dominik Slezak
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ABSTRACT:
The theory of rough sets provides clear mathematical and algorithmic foundations for handling incompleteness and uncertainty in massive amounts of data. Rough set methods are often utilized in data mining and knowledge discovery in order to induce various types of decision and classification models. On the one hand, there are a number of approaches to feature selection, which refers to the notion of a decision reduct developed within the theory of rough sets for the purpose of describing irreducible subsets of features determining decisions at roughly the same level as all attributes. On the other hand, there are approximated versions of computational models known from data mining and machine learning, such as rough clustering, rough support vector machines, or rough neural networks.
In this talk, we refer to both above trends in rough set research and applications. With regard to the latter one, we show how rough set paradigms of computing with approximations can be used to scale standard calculations over huge volumes of data. As a practical case study, we examine Infobright’s analytical RDBMS technology based on hybridization of the principles of columnar stores and rough computing. With regard to the former out of the above-mentioned trends, we show several extensions of decision reducts developed for different purposes, such as ensemble classifier learning and stream data processing. In all cases, we pay a special attention to scalability of the underlying computations.
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Dr Shampa Chakraverty
Professor & Head, Deptt. of Computer Engineering
Netaji Subhas Institute of Technology
Dwarka, Sector 3, New Delhi-110078
Phone: 91-011-25099062(O)
09899568694 (M)