Klaster analiza odabranih gospodarskih i socioekonomskih pokazatelja zemalja EU

master's thesis

• Title (english): Cluster analysis of selected economic and socioeconimic indicators of EU countries
• Author: Zvonimir Galir
• Mentor: Mirjana Pejić Bach (mentor)
• Committee member: Mirjana Pejić Bach (predsjednik povjerenstva)
• Granter: University of Zagreb; Faculty of Economics and Business; (Department of Informatics); Zagreb
• Defense date and country: 2024-09-16, Croatia
• Scientific / art field, discipline and subdiscipline: SOCIAL SCIENCES; Economics; Business Informatics

Abstract

U diplomskom radu je korištena metoda strojnog učenja klaster analiza kako bi se ispitala ekonomska aktivnost zemalja Europske unije. Kao podaci u analizi korišteni su podaci o gospodarskim i socioekonomskim pokazateljima kroz dva razdoblja, od najnovijih podataka prema razdoblju u nazad deset godina. Optimalan broj klastera u svakoj analizi određen je metodom lakta.
U gospodarske pokazatelje su ubrojeni: bruto domaći proizvod po glavi stanovnika, rast bruto domaćeg proizvoda, uvoz, izvoz, paritet kupovne moći , a u socioekonomske: nezaposlenost, Gini koeficijent, rizik od siromaštva. Klaster analizom se promatrala promjena pozicija zemalja u klasterima u odnosu na vremenska razdoblja te su doneseni zaključci o ekonomskim kretanjima. U
teorijskom dijelu su objašnjene metode otkrivanja znanja iz baza podataka a posebno klaster analiza, dok su u praktičnom dijelu tablice dobivene rezultatima iz programa.

Abstract (english)

In this Master's thesis, the machine learning method cluster analysis was used to examine the economic activity of European Union countries. Data on economic and socio-economic indicators spanning two periods, from the most recent data to the past ten years were used in the analysis. The optimal number of clusters in each analysis was determined by the elbow method. Economic indicators included in the analysis were: gross domestic product, GDP per capita, import, export, inflation, while socio-economic indicators included unemployment, the Gini coefficient, and the risk of poverty. Cluster analysis was used to observe changes in the positions of countries within clusters over time periods, which led to conclusions about economic trends. The theoretical section explained knowledge discovery methods from databases, with a special focus on cluster analysis, while the practical section presented tables derived from the program's results.

• Keywords: Klaster analiza; Weka; metoda lakta; ekonomski pokazatelji
• Keywords (english): Cluster analysis; Weka; elbow method; economic indicators
• Language: croatian
• URN:NBN: urn:nbn:hr:148:226738
• Study programme: Title: Business economics; Study programme type: university; Study level: integrated undergraduate and graduate; Academic / professional title: sveučilišni magistar ekonomije/sveučilišna magistra ekonomije (sveučilišni magistar ekonomije/sveučilišna magistra ekonomije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Access restricted to students and staff of home institution
• Created on: 2024-10-01 15:12:42