Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application

Abstract

Purpose

The paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.

Design/methodology/approach

The paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.

Findings

The paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.

Originality/value

The new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.

Citation

Wu, H. and Qu, Z. (2020), "Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application", Grey Systems: Theory and Application, Vol. 10 No. 4, pp. 413-423. https://doi.org/10.1108/GS-09-2019-0040