Data Mining of World Bank Indicators

Maha A. Hana ()
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Maha A. Hana: Canadian International College (deputed from Department of Information Systems, Faculty of Computers and Information, Helwan University)

A chapter in Strategic Innovative Marketing, 2017, pp 521-527 from Springer

Abstract: Abstract World Bank annual report contains vital data indicators about many countries. Data mining techniques helps in studying the underlying relation between different indicators. This research proposes a clustering system for Egypt’s World Bank indicators. The proposed system has three phases; preprocessing phase, clustering phase and analysis phase. Preprocessing phase consolidates Egypt’s data and prepares it for clustering. Clustering phase estimates the appropriate number of clusters and uses K-Means to cluster both years’ data and indicators data values. Analysis phase uses principle component analysis to find the most important indicators for each type of cluster. The results indicate that years’ clusters are more compact and separated than indicators’ clusters.

Keywords: Clustering techniques; Data mining; Principal component analysis; World Bank Indicators (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-56288-9_69

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DOI: 10.1007/978-3-319-56288-9_69

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