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.

中文翻译：

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基于动态加权关联度的面板数据灰色聚类模型及其应用

目的

本文旨在为面板数据提出一个聚类模型。更具体地说，本文旨在构建面板数据的灰色关联模型，以解决具有多因素和多属性的分类。

设计/方法/方法

本文选择了基于动态加权函数的灰色关联理论进行聚类理论研究。本文通过实例验证了新模型的合理性，表明新模型可以反映面板数据的发生程度。

发现

本文提供了一种基于动态加权函数的新灰色关联模型，该模型可以在一定程度上放大样本的特征。新的入射模型的属性（例如归一化，对称性和邻近性）都得到满足。本文还表明，新的发病率模型在聚类判别方面表现很好。

创意/价值

本文的新模型对面板数据的灰色关联度分析理论进行了补充和改进。