Semantic clustering of the World Bank data † This research was partially supported by the grant No. 201/04/2102 of the GA ČR, by the Program “Information Society” under project 1ET100300517 and by the Grant Agency of Charles University in Prague under Grant No. 358/2006/A-INF/MFF.

Citation data:

International Journal of General Systems, ISSN: 0308-1079, Vol: 37, Issue: 4, Page: 417-442

Publication Year:
2008
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Repository URL:
http://scholarsmine.mst.edu/engman_syseng_facwork/135
DOI:
10.1080/03081070701210345
Author(s):
Iveta, Mrázová; Dagli, Cihan H.
Publisher(s):
Informa UK Limited; Taylor & Francis
Tags:
Engineering; Mathematics; Computer Science; 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; 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; Operations Research, Systems Engineering and Industrial Engineering
article description
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.