Grey Incidence Analysis Models for Matrix Data and Matrix Sequences Data

The purpose of this paper is to construct a new grey incidence analysis model for matrix data and matrix sequences data. Firstly, there is a summary for existing grey incidence analysis models of matrix data, and the grey incidence clustering analysis is achieved by some existing grey incidence analysis models of matrix data for dataset LP1 of the UCI machine learning database. Secondly, according to the test results, the absolute degree of grey incidence model in the paper [1] can be improved in the numerical calculation by theoretical analyzing. Hence, a new grey incidence model of matrix data is proposed in this paper, which can be successfully applied to the grey incidence clustering analysis for the dataset LP1 of UCI. With the same analysis method, a new grey incidence analysis model of matrix sequence data based on hyper-volume form is constructed. Further, the new grey incidence analysis model of matrix sequence data is used to distinguish the sensitivity of different sensors. By comparing with Deng's degree of grey incidence model and norm degree of grey incidence model, it shows that the new grey incidence analysis model of matrix sequence data defined in this paper is applicable.