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.
Recommended Citation
M. Iveta and C. H. Dagli, "Semantic Clustering of the World Bank Data," International Journal of General Systems, Taylor & Francis, Aug 2008.
The definitive version is available at https://doi.org/10.1080/03081070701210345
Department(s)
Engineering Management and Systems Engineering
Sponsor(s)
Charles University
Czech Republic. Grant Agency
Keywords and Phrases
Artificial Intelligence; Cluster Validity; Data Mining; Feature Selection; Fuzzy Clustering; Fuzzy Systems; General Systems; Intelligent Systems; Production Systems & Automation; Semantics Assignment; Systems & Computer Architecture; Systems & Control Engineering; Systems Biology
International Standard Serial Number (ISSN)
0308-1079
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2008 Taylor & Francis, All rights reserved.
Publication Date
01 Aug 2008
