The K-Nearest Neighbor classifier is a well-known and widely applied method in data mining applications. Nevertheless, its high computation and memory usage cost makes the classical K-NN not feasible for today’s Big Data analysis applications. To overcome the cost drawbacks of the known data mining methods, several distributed environment alternatives have emerged. Recently, several K-NN based classification algorithms have been proposed which are distributed methods suitable for distributed computing environments and applicable for emerging data analysis needs. In this work, a new CR-KNN algorithm is proposed, which improves the classification accuracy performance of the well-known KNearest Neighbor (K-NN) algorithm by benefiting from the center representation of the instances belonging to different data classes. The proposed algorithm relies on the data class representations which are the closest to the test instance. The CR-KNN algorithm was tested using several real-datasets belonging to different application areas. The performance results acquired after extensive experiments are presented in this paper and they prove that the proposed CR-KNN algorithm is a competitive alternative to other studies recently proposed in the literature.