Graphical Table of Contents for Library Collections: The Application of Universal Decimal Classification Codes to Subject Maps

<span>The representation of information content by graphical maps is an extended ongoing research topic. The objective of this article consists in verifying whether it is possible to create map displays using Universal Decimal Classification (UDC) codes (using co-classification analysis) for the purpose of creating a graphical table of contents for a library collection. The application of UDC codes was introduced to subject maps development using the following graphic representation methods: (1) multidimensional scaling; (2) cluster analysis; and (3) neural networks (self-organizing maps). Finally, the authors conclude that the different kinds of maps have slightly different degrees of viability and types of application.</span>

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Application of Kohonen networks for clustering of the Zachodniopomorskie Voivodeship districts in terms of the level of socio-economic development

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu ◽

10.15611/pn.2020.9.08 ◽

2020 ◽

Vol 64 (9) ◽

pp. 100-118

Author(s):

Joanna Perzyńska

Keyword(s):

Neural Networks ◽

Cluster Analysis ◽

Economic Development ◽

Statistical Data ◽

Multidimensional Analysis ◽

Diagnostic Features ◽

Self Organizing Maps ◽

Socio Economic Development ◽

Kohonen Networks ◽

Theoretical Considerations

The author presents the possibilities of using artificial neural networks in a multidimensional analysis – cluster analysis. The empirical example using districts of the Zachodniopomorskie (West Pomeranian) Voivodeship is the illustration of theoretical considerations. The study used statistical data from many areas related to socio-economic development: demography, labour market, natural environment, recreation, culture, social and technical infrastructure, and the economy. The aim of the study was to divide the voivodeship into disjointed typological groups of districts using Kohonen networks (Self-Organizing Maps). Several networks differing in structure of the output layer were constructed and trained. Selected diagnostic features of socio-economic development of districts were their input values. Using verified Kohonen networks, various sets of groups of the researched objects were created, and confirmed them are a useful tool for identifying clusters of districts similar to each other in terms of the level of socio-economic development.

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