Clustering Over Multiple Evolving Data Streams of the Traffic Cyber-Physical Systems

Abstract

Extracting and retaining useful information from multiple data streams of the traffic Cyber-Physical Systems (CPS) generated by practical applications have attracted an increasing amount of attentions from related researchers. In this paper, the Incremental Clustering framework is proposed for multiple sensor data streams by low rank approximation Matrix Factorization (IC-MF) monitors the distribution of clusters over multiple sensor data streams based on their correlation. In the IC-MF, both the low-rank matrix approximation and matrix factorization-based clustering are applied, and IC-MF incorporates the historical results and the relationship between the nodes of the current step and previous step. To improve the accuracy of the increments, a low-rank approximation of the adjacency matrix is obtained at each time step, and makes IC-MF work directly in the low-rank subspace. The main idea of IC-MF is to make use of the similarity between two consecutive time steps to quickly update the approximating subspace. The performance and efficiency of the algorithm are demonstrated by traffic CPS experiments on the real and synthetics data sets. The results show that the proposed algorithm surpasses the existing methods for clustering multiple evolving data streams over time.