论文标题
符号多边形数据的新聚类方法：应用于企业家制度的聚类
New clustering approach for symbolic polygonal data: application to the clustering of entrepreneurial regimes
论文作者
论文摘要
创业制度是主题，受到更多的研究关注。现有关于企业家制度的研究主要使用多元分析和某种类型的机构相关分析中的常用方法。在我们的分析中，通过采用新型的多边形数据群集分析方法来分析企业家制度。考虑到符号数据分析（SDA）中数据结构的多样性，间隔值数据是最流行的。然而，这种方法需要假设等分。我们使用一种新型的多边形群集分析方法来解决此限制，并具有其他优点：存储更多信息，大大减少通过多边形半径来保存经典可变性的大型数据集，并在符号数据分析中打开新的可能性。我们为这种类型的数据构建了动态群集分析算法，该算法证明了主要的定理和lemmata，以证明其使用情况是合理的。在经验部分中，我们使用2015年全球企业家监测仪（GEM）数据集来根据对主要企业家问题的回答来构建国家的类型。本文在统计理论（从未考虑过新型变量）中提出了一种新颖的聚类方法，并在企业家精神中应用了新的问题，并具有新的结果。
Entrepreneurial regimes are topic, receiving ever more research attention. Existing studies on entrepreneurial regimes mainly use common methods from multivariate analysis and some type of institutional related analysis. In our analysis, the entrepreneurial regimes is analyzed by applying a novel polygonal symbolic data cluster analysis approach. Considering the diversity of data structures in Symbolic Data Analysis (SDA), interval-valued data is the most popular. Yet, this approach requires assuming equidistribution hypothesis. We use a novel polygonal cluster analysis approach to address this limitation with additional advantages: to store more information, to significantly reduce large data sets preserving the classical variability through polygon radius, and to open new possibilities in symbolic data analysis. We construct a dynamic cluster analysis algorithm for this type of data with proving main theorems and lemmata to justify its usage. In the empirical part we use dataset of Global Entrepreneurship Monitor (GEM) for year 2015 to construct typologies of countries based on responses to main entrepreneurial questions. The article presents a novel approach to clustering in statistical theory (with novel type of variables never accounted for) and application to a pressing issue in entrepreneurship with novel results.
