Objective: The purpose of this article is to show the value of exploratory data analysis performed on the multivariate time series dataset of gross domestic products per capita (GDP) of 160 countries for the years 1970-2010. New knowledge can be derived by applying cluster analysis to the time series of GDP to show how patterns in GDP can be explained in a data-driven way. Research Design & Methods: Patterns characterised by distance and density based structures were found in a topographic map by using dynamic time warping distances with the Databionic swarm (DBS) . The topographic map represents a 3D landscape of data structures. Looking at the topographic map, the number of clusters was derived. Then, a DBS clustering was performed and the quality of the clustering was verified. Findings: Two clusters are identified in the topographic map. The rules deduced from classification and regression tree (CART) show that the clusters are defined by an event occurring in 2001 at which time the world economy was experiencing its first synchronised global recession in a quarter-century. Geographically, the first cluster mostly of African and Asian countries and the second cluster consists mostly of European and American countries. Implications & Recommendations: DBS can be used even by non-professionals in the field of data mining and knowledge discovery. DBS is the first swarm-based clustering technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organisation, and game theory. Contribution & Value Added: To the knowledge of the author it is the first time that worldwide similarities between 160 countries in GDP time series for the years 19702010 have been investigated in a topical context. Article type: research article

中文翻译：

---

人均国内生产总值聚类分析

目标：本文的目的是展示对 1970-2010 年 160 个国家人均国内生产总值 (GDP) 的多元时间序列数据集进行探索性数据分析的价值。通过将聚类分析应用于 GDP 的时间序列，可以得出新知识，以展示如何以数据驱动的方式解释 GDP 中的模式。研究设计和方法：通过使用数据仿生群 (DBS) 的动态时间扭曲距离，在地形图中发现了以基于距离和密度的结构为特征的模式。地形图表示数据结构的 3D 景观。查看地形图，得出了集群的数量。然后，进行 DBS 聚类并验证聚类的质量。结果：在地形图中确定了两个集群。从分类和回归树 (CART) 推导出的规则表明，集群是由 2001 年发生的事件定义的，当时世界经济正经历 25 年来首次同步的全球衰退。从地域上看，第一个集群主要由非洲和亚洲国家组成，第二个集群主要由欧洲和美洲国家组成。启示和建议：即使是数据挖掘和知识发现领域的非专业人士也可以使用 DBS。DBS 是第一个基于群的聚类技术，它在利用群智能、自组织和博弈论的概念的同时显示出涌现的特性。贡献和增值：据作者所知，这是第一次在主题背景下调查 160 个国家在 19702010 年的 GDP 时间序列中的全球相似性。文章类型：研究文章