Title

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.

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

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 Taylor & Francis, All rights reserved.


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