@ARTICLE { Rsds1951,
author = {Hassanien, Aboul}, 
title = {Rough Set Approach for Attribute Reduction and Rule Generation: A Case of Patients with Suspected Breast Cancer}, 
journal = {Journal of the American Society for Information Science and Technology (JASIST)}, 
number = 	{(no. 11)}, 
volume = 55, 
pages = {954-962}, 
year = 2004, 
abstract = {Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach for attribute reduction and generating classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. In order to evaluate the validity of the rules based on the approximation quality of the attributes, we introduce statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.}, 
keywords = {data mining, rough sets, breast cancer analysis, rule generation and reduction, decision trees, intelligent data analysis, rule classification, }, 
}