Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators miss key information on the relationship between different indicators. In particular, the grouping of indicators via topics is not reflected in the data at a global and local level. We overcome these issues by using the clustering of indicators to build a new set of cluster driven composite development indicators that are objective, data driven, comparable between countries, and retain interpretabilty. We discuss their consequences on informing policy makers about country development, comparing them with the top PageRank indicators as a benchmark. Finally, we demonstrate that our new set of composite development indicators outperforms the benchmark on a dataset reconstruction task.

中文翻译：

---

一组新的集群驱动的综合发展指标

决策中使用的综合发展指标通常在主观上汇总了一组有限的指标。我们使用降维技术（包括主成分分析（PCA））以及首次进行信息过滤和层次聚类来显示，这些复合指标缺少有关不同指标之间关系的关键信息。特别是，通过主题对指标进行分组不会反映在全局和本地级别的数据中。我们通过使用指标的聚类来建立一组新的由聚类驱动的综合发展指标，从而克服了这些问题，这些指标是客观的，数据驱动的，在国家之间具有可比性并且保留了可解释性。我们讨论了它们在通知决策者有关国家发展，将它们与排名靠前的PageRank指标进行比较。最后，我们证明了我们的一组新的复合开发指标优于数据集重建任务的基准。