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In this paper, we use the set-membership normalized least-mean-square (SM-NLMS) algorithm to censor the data set in big data applications. First, we use the distribution of the noise signal and the excess of the steady-state mean-square error (EMSE) to estimate the threshold for the desired update rate in the single threshold SM-NLMS (ST-SM-NLMS) algorithm. Then, we introduce the double threshold SM-NLMS (DT-SM-NLMS) algorithm which defines an acceptable
range of the error signal. This algorithm censors the data with very low and very high output estimation error.
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