Kohonen SOM Learning Strategy and Country Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00316574" target="_blank" >RIV/68407700:21340/17:00316574 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Kohonen SOM Learning Strategy and Country Classification

Original language description

The Self-Organized Mapping (SOM) represents a traditional tool for multidimen- sional data analysis overperforming analytical power of cluster analysis. But there are possible difficulties when the SOM is applied to data patterns of large size. We present testing example using iris dataset. Our approach is mainly used for macro-economical data analysis which is based on logarithmic differences, pattern dimensionality reduction and finalization of data analysis using Kohonen SOM learning. General methodology was applied to main economic indicators describing the situation of thirty five countries during more than twenty years. The used dataset comes from regularly published statistics of European Commission. The main aim is to identify the similarities of countries. The role of SOM topology, learning strategy and reduced pattern size can be also used to predict behaviour during crisis based on the identified similarity and known.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů