GDP growth vs. criminal phenomena : data mining of Japan 1926–2013
| Autor: | Xingan Li, Martti Juhola, Henry Joutsijoki, Jorma Laurikkala |
|---|---|
| Přispěvatelé: | Luonnontieteiden tiedekunta - Faculty of Natural Sciences, University of Tampere |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Self-organizing map
Multivariate statistics Computer science development of criminal phenomena Separation (statistics) 0211 other engineering and technologies Process improvement crime rate 02 engineering and technology computer.software_genre GDP growth rate Gross domestic product self-organizing map classification methods Japan Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Tietojenkäsittely ja informaatiotieteet - Computer and information sciences Cluster analysis 021110 strategic defence & security studies business.industry data mining Human-Computer Interaction Philosophy Business intelligence Crime rate 020201 artificial intelligence & image processing Data mining business computer |
| Popis: | The aim of this article is to inquire about potential relationship between change of crime rates and change of gross domestic product (GDP) growth rate, based on historical statistics of Japan. This national-level study used a dataset covering 88 years (1926–2013) and 13 attributes. The data were processed with the self-organizing map (SOM), separation power checked by our ScatterCounter method, assisted by other clustering methods and statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in research of criminal phenomena through processing of multivariate data. The research confirmed previous findings that SOM was able to cluster efficiently the present data and characterize these different clusters. Other machine learning methods were applied to ensure clusters computed with SOM. The correlations obtained between GDP and other attributes were mostly weak, with a few of them interesting. |
| Databáze: | OpenAIRE |
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