The use of Rough Set methods in KDD

Knowledge Discovery in Databases (KDD) is a process involving many steps, including data preparation, data cleaning, data mining, and data/result visualisation. In this short tutorial our goal will be to present a hands-on guide for using methods and algorithms that originated in the area of Rough Sets (RS) for the purposes of KDD. We will try to answer the common issue of choosing the right method for a given set of data and convince the audience that in some situations the algorithms originating in RS theory are best suited for the job.  We will also presnts some of the existing implementations and tools for using RS methods in KDD. As a conclusion we will point out possible new trends in both basic and applied research on using RS methods in KDD.