Pattern selection encompasses pinpointing a subsection of the most important features that is well-suited results as classification features. A pattern selection algorithm may be appraised from both the good organization and usefulness points of view. Although the good organization concerns the time necessary to discover a subsection of pattern, the usefulness is related to the excellence of the subsection of features. Latest methodologies for classification data are based on metric resemblances. To reduce unfairness measures using graph-based algorithm to replace this process in this project using more recent approaches like Affinity Propagation (AP) algorithm can take as input also general non metric similarities.