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

In order to fully excavate the information contained in the multi-index panel data, one take decision objects as the research object, and 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 indexes are given. based on grey incidence analysis, the absolute difference and relative difference between the measure 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, and then according to the grey incidence degree between the objects, the objects can be clustered based on 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.