Research Techniques Derived From Rough Sets Theory: Rough Classification and Rough Clustering

Research techniques are often divided into quantitative approaches, concerned with measurement, and qualitative approaches, concerned with meaning. Methodologists have often found it difficult to reconcile this division. However, computational intelligence is directly concerned with capturing meaning in a rigorous form. One commonly used technique is rough sets theory, which analyses an information system consisting of a set of objects. The concept of an object is broad enough to include both qualitative and quantitative data structures. This paper provides an overview of rough sets theory, and shows how the theory can be used in both classification and clustering tasks. It presents an analysis of a number of simplified data sets that demonstrate how rough sets theory can be applied to problems in business research.