A novel grey object matrix incidence clustering model for panel data and its application

In order to fully excavate the information contained in multi-index and small sample panel data, one takes decision objects as the research object. The development state matrix and the development speed matrix of the decision objects are defined by considering the cross-section information and time information of the decision objects, and then the distances among the objects over the indices are given. Based on grey incidence analysis, the absolute difference and relative difference between the measured value matrices are used to characterize and measure the close degree of the development state matrix and the development level matrix of the decision objects, so that the grey object matrix absolute incidence analysis model is established. Subsequently, according to the grey incidence degree between the objects, the objects can be clustered based on the hierarchical clustering algorithm. Finally, a clustering problem of regional patent research and development (R&D) efficiency is used to verify the validity and rationality of the proposed model. (C) 2021 Sharif University of Technology. All rights reserved.