Data Stream Adaptive Partitioning of Sliding Window Based on Gaussian Restricted Boltzmann Machine

Abstract

In this paper, the adaptive partitioning problem of data stream under sliding window is discussed. Gaussian restricted Boltzmann machine (GRBM) model supporting decimal input is proposed, which can be trained through iteration for data reconstruction subsequently. At the same time, a data stream adaptive block algorithm based on Kullback–Leibler divergence (KL distance) is proposed to compare the probability distribution difference in the sliding window. Then, obtain the predicted value by the distribution of the previous data and determine whether the KL distance is within the confidence interval, so as to realize the adaptive adjustment of the sliding window, and the divided of data stream.