Optimistic Rough Sets Attribute Reduction using Dynamic Programming

Nowadays, and with the current progress in technologies and business sales, databases with large amount of data exist especially in Retail Companies. The main objective of this study is to reduce the complexity of the classification problems while maintaining the prediction classification quality. We propose to apply the promising technique Rough set theory which is a new mathematical approach to data analysis based on classification of objects of interest into similarity classes, which are indiscernible with respect to some features. Since some features are of high interest, this leads to the fundamental concept of “Attribute Reduction”. The goal of Rough set is to enumerate good attribute subsets that have high dependence, discriminating index and significance. The naïve way of is to generate all possible subsets of attribute but in high dimension cases, this approach is very inefficient while it will require 1 2  d iterations. Therefore, we propose the Dynamic programming technique in order to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. Implementation has been developed, applied, and tested over a 3 years historical business data in Retail Business (RB). Simulations and visual analysis are shown and discussed in order to validate the accuracy of the proposed tool. KeywordsData Mining; Business Retail; Rough Sets; Attribute Reduction; Classification; Dynamic Programming