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Dynamic discreduction using Rough Sets
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DOI：10.1016/j.asoc.2011.01.015.png)
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Abstract
En 中文
Discretization of continuous attributes is a necessary pre-requisite in deriving association rules and discovery of knowledge from databases. The derived rules are simpler and intuitively more meaningful if only a small number of attributes are used, and each attribute is discretized into a few intervals. The present research paper explores the interrelation between discretization and reduction of attributes. A method has been developed that uses Rough Set Theory and notions of Statistics to merge the two tasks into a single seamless process named dynamic discreduction. The method is tested on benchmark data sets and the results are compared with those obtained by existing state-of-the-art techniques. A real life data on TRIP steel is also analysed using the proposed method. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Rough Set
Discretization
Classification
Data mining
TRIP steel
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