A Tensor-Based Multiple Clustering Approach With Its Applications in Automation Systems

Abstract

Multiple clustering analysis has the clear advantages to discover latent data pattern in big data from different views, so it has tremendous practical values in automation industries. However, most of current algorithms are difficult to group heterogeneous data to multiple clusterings according to the requirements of different applications. This paper presents a flexible multiple clustering analytic and service framework, and a novel tensor-based multiple clusterings (TMC) approach. Heterogeneous data objects in cyber-physical-social systems are first represented as low-order tensors and a weight tensor construction approach is proposed to measure the importance of attributes combinations in heterogeneous feature spaces. Then, a selective weighted tensor distance is explored to cluster tensorized data objects for different applications. This paper, through a real-world smart bike maintenance system, illustrates TMC and evaluates its clustering performance. Experimental results reveal TMC can obtain higher quality clustering results but with lower redundancies to meet different requirements of applications in automation systems.