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This study suggests a novel clustering method using entropy in information theory for setting cut-scores.Based on item response vectors from the examinees,we construct the Ordered Item Booklets(OIBs)based on the Rasch model which is a kind of Item Response Theory(IRT).The approach of the proposed method is to partition the scores into n-clusters and to construct probability distribution tables separately for each cluster from the item response vector.Using these probability distribution tables,mutual information and relative entropy(Kullback-leibler divergence)were computed between each of the clusters and then cut-scores were determined by the clusters partition to minimize mutual information values.Experimental results show that the approach of this proposed entropy method has a realistic possibility of application as a clustering evaluation method.