Test case selection is to minimize the time and effort spent for software testing in real time practice. During the course of software testing, the software firms are in want of techniques to finish the testing in a stipulated time, whilst uncompromising on quality. The motto is to select subset of test cases rather to take up all available test cases to uncover most of the bugs. Clustering of test cases using ranking and also based on similarity coefficients is to be implemented. The experimented results have to show up the techniques proposed improving the catching up of errors in a comparatively shorter duration. In this research, eleven different features were considered in order to cluster the test cases. There are two methodologies implemented. In the first methodology, each cluster will cover set of specific features to a certain percentage. Depending on the feature’s coverage, cluster of test cases can be selected. These clusters were formed using ranking methodology. In the second methodology, similarity among test cases based on eleven features is found. Then max-min composition is used to find fuzzy equivalences, upon which clusters are formed. Most similar test cases are clustered.