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Description
| - The paper deals with the problem of searching the efficient frontier and group of similar units using two methods – multivariate Cluster Analysis and multicriteria Data Envelopment Analysis. The main aim of the paper is to propose the way how to choose an optimal number of groups coming to empirical analysis. Firstly it´s necessary to find the closest characteristics for a given unit according to a previously specified criterion of similarity. After fulfilment this criterion and creating optimal groups of similar units, it´s possible to evaluate level of efficiency of homogenous NUTS 2 regions within “new” EU Member States based on Regional Competitiveness Index approach; efficiency is thus seen as a mirror of competitiveness. The idea laying behind this approach is that inefficient regions can learn more easily from those, that are more similar; and closer similarity may show for the inefficient regions how to achieve the improvement with less effort.
- The paper deals with the problem of searching the efficient frontier and group of similar units using two methods – multivariate Cluster Analysis and multicriteria Data Envelopment Analysis. The main aim of the paper is to propose the way how to choose an optimal number of groups coming to empirical analysis. Firstly it´s necessary to find the closest characteristics for a given unit according to a previously specified criterion of similarity. After fulfilment this criterion and creating optimal groups of similar units, it´s possible to evaluate level of efficiency of homogenous NUTS 2 regions within “new” EU Member States based on Regional Competitiveness Index approach; efficiency is thus seen as a mirror of competitiveness. The idea laying behind this approach is that inefficient regions can learn more easily from those, that are more similar; and closer similarity may show for the inefficient regions how to achieve the improvement with less effort. (en)
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Title
| - NUTS 2 Regions Classification: Comparison of Cluster Analysis and DEA Method
- NUTS 2 Regions Classification: Comparison of Cluster Analysis and DEA Method (en)
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skos:prefLabel
| - NUTS 2 Regions Classification: Comparison of Cluster Analysis and DEA Method
- NUTS 2 Regions Classification: Comparison of Cluster Analysis and DEA Method (en)
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skos:notation
| - RIV/61989100:27510/14:86090724!RIV15-MSM-27510___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/61989100:27510/14:86090724
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Classification, cluster, cluster analysis, DEA method, efficient frontier, NUTS 2 region, RCI (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - XVII. mezinárodní kolokvium o regionálních vědách : sborník příspěvků : Hustopeče, 18.-20. června 2014
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Melecký, Lukáš
- Staníčková, Michaela
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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number of pages
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http://bibframe.org/vocab/doi
| - 10.5817/CZ.MUNI.P210-6840-2014-4
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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