Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce  substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of  rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with  hand examples, the book may also serve as a textbook.