Dynamic discreduction using Rough Sets

作者：Dey P^*; Dey S; Datta S; Sil J

来源：Applied Soft Computing, 2011, 11(5): 3887-3897.

DOI：10.1016/j.asoc.2011.01.015

• Rough Set
• Discretization
• Classification
• Data mining
• TRIP steel

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.

• 出版日期2011-7