Maximum correntropy criterion(MCC)provides a robust optimality criterion for non-Gaussian signal processing.In this paper,the weight update equation of the conventional MCC-based adaptive filtering algorithm is modified by reusing the past K input vectors,forming a class of data-reusing MCC-based algorithm,called DRMCC algorithm.Comparing with the conventional MCCbased algorithm,the DR-MCC algorithm provides a much better convergence performance when the input data is correlated.The mean-square stability bound of the DRMCC algorithm has been studied theoretically.For both Gaussian noise case and non-Gaussian noise case,the expressions for the steady-state Excess mean square error(EMSE) of DR-MCC algorithm have been derived.The relationship between the data-reusing order and the steadystate EMSEs is also analyzed.Simulation results are in agreement with the theoretical analysis. Maximum correntropy criterion (MCC) provides a robust optimality criterion for non-Gaussian signal processing. In this paper, the weight update equation of the conventional MCC-based adaptive filtering algorithm is modified by reusing the past K input vectors, forming a class of data -reusing MCC-based algorithm, called DRMCC algorithm. Comparing with the conventional MCC based algorithm, the DR-MCC algorithm provides a much better convergence performance when the input data is correlated. The mean-square stability bound of the DRMCC algorithm has been studied theoretically For both Gaussian noise case and non-Gaussian noise case, the expressions for the steady-state Excess mean square error (EMSE) of DR-MCC algorithm have been derived. The relationship between the data-reusing order and the steadystate EMSEs is also analyzed.Simulation results are in agreement with the theoretical analysis.