Computational approach to predict peptide binding to major histocompatibility complex (MHC) is crucial for vaccine design since these peptides can act as a T-Cell epitope to trigger immune response. There are two main branches for peptide prediction methods; structural and data mining approaches. These methods can be successfully used for prediction of T-Cell epitopes in cancer, allergy and infectious diseases. In this paper, association rule mining methods are implemented to generate rules of peptide selection by MHCs. To capture the binding characteristics, modified rule mining and data transformation methods are implemented in this paper. Peptides are known to bind to the same MHC show sequence variability, to capture this characteristic, we used a reduced amino acid alphabet by clustering amino acids according to their physico-chemical properties. Using the classification of amino acids and the OR-operator to combine the rules to reflect that different amino acid types and positions along the peptide may be responsible for binding are the innovations of the method presented. We can predict MHC Class-I binding with 75-97% coverage and 76-100% accuracy.