Constructing Social And Economic Indicators
For EU Countries Using Dynamic Classification:
Case Studies
Author(s)
I. Gertsbakh, I. Yatskiv & O. Platonova
Abstract
This paper presents applications of the dynamic classification algorithm (DCA)
described in Gertsbakh and Yatskiv (Dynamic Classification: Economic Welfare
Growth in EU During 1995-2004. Proceedings of International Conference
\“Data Mining 2006”, 11-13 July 2006, Prague. WIT Press. 2006, p.53-62) to the
development of three socio-economic indicators: National Health Index (NHI),
Population Mobility Index (PMI) and Logistic Performance Index (LPI). In each
of these three cases we work with the respective data for EU-25 countries. The
essence of the DCA is to transform a multidimensional vector to a scalar on the
basis of combining cluster and discriminant analysis. In Gertsbakh and Yatskiv,
the DCA was used for obtaining a measure of socio-economic welfare and its
dynamics for EU-25 countries over the period 1995-2004. The output of DCA is
a collection of time series (graphs) representing the dynamics and the pattern of
the scalar socio-economic indicator for each country in the considered time
period.
Keywords: cluster analysis, Fisher discriminant function, time series, socioeconomic
indicators.
1 Introduction: the data and the algorithm
The data. The row data are given as a three-dimensional array of entries having
the following form:
)], ( ),..., ( ),..., ( [ ) ; ,..., 1 ; ( 1 t x t x t x t k j i ik ij i = = y (1)
Keywords
cluster analysis, Fisher discriminant function, time series, socioeconomic
indicators.
Related Book
Other papers in this volume
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