A methodical approach to data mining increases the chances of overcoming those barriers. Database selection and preparation. To mine data effectively, the warehouse must be set up properly. The first step is to identify the databases and factors to be explored. If possible, a live data dictionary should be created from which required records can be retrieved into the flat files needed by most analysis routines.
Data preparation involves filling in missing values and correcting errors.
The referential integrity controls of modern relational databases have improved data quality, but legacy databases may be incomplete or full of errors. Interpolating missing data can be dangerous, particularly when dealing with small samples. Clustering and feature analysis. The large database groups defined during the preparation phase are divided further using clustering techniques. That is followed by a more detailed feature analysis to find the factors that most obviously contribute to the formation of the clusters and to determine which factors are involved in attaining particular business goals.