alexa Abstract | A Survey: Rough Set Theory in Incomplete Information Systems
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


Rough Set theory has been conceived as a tool to conceptualize, organize and analyze various types of data, in particular, to deal with inexact, uncertain or vague knowledge in applications related to Artificial Intelligence. In the case of classification, this theory implicitly calculates reducts of the full set of attributes, eliminating those that are redundant or meaningless. Such reducts may even serve as input to other classifiers other than Rough Sets. The typical high dimensionality of current databases precludes the use of greedy methods to find optimal or suboptimal reducts in the search space and requires the use of stochastic methods. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits that rough sets can provide for data mining and related tasks, efficient computation of these approximations is vital.

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Author(s): M. Pushpalatha, Dr. V. Anuratha


Rough set, Artificial Intelligence, greedy method, data mining, and approximations., Autonomic and Context Aware Computing,Bioinformatics and Computational Biology,Broadband and Intelligent Networks,Calm Technology,CDMA/GSM Communication Protocol

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