Semantic clustering of the World Bank data

Abstract

World Development Indicators (WDI) published annually by the World Bank provide comparative socio-economic data for state economies. Several countries show common trends in their development. But to understand these trends in the development process, an appropriate interpretation of the intrinsic similarities has to be found. In this paper, we propose a novel approach to assigning an adequate semantics to clusters formed by fuzzy c-means clustering. Despite of the ability to identify unique characteristics for the found clusters, the introduced fuzzy c-landmarks show a great potential for dimension reduction and for simplified data set descriptions. Experiments performed so far confirm efficient processing for this kind of exploratory data analysis.