The availability of massive amounts of experimental data based on genome-wide association and mass spectroscopy studies have given motivation in recent years to a large effort in developing mathematical, statistical and computational techniques to infer biological models from data. In many bioinformatics problems the number of features is significantly larger than the number of samples (high feature to sample ratio data sets) and feature selection techniques have become an apparent need in many bioinformatics applications.

The availability of huge amounts of data resulted in great need of data mining technique in order to generate useful knowledge. In the present study we provide detailed information about data mining techniques with more focus on classification techniques as one important supervised learning technique. We also discuss WEKA software as a tool of choice to perform classification analysis for different kinds of available data. A detailed methodology is provided to facilitate utilizing the software by a wide range of users.