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Title:Pravičnost gručenja k-means : magistrsko delo
Authors:ID Praprotnik, Patrik (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
ID Colakovic, Ivona (Comentor)
Files:.pdf MAG_Praprotnik_Patrik_2025.pdf (5,22 MB)
MD5: 8B29506C38E15C19D520196AFD6CCE64
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Naloga obravnava problem pravičnosti v algoritmu gručenja k-means in njegovih prilagojenih različicah. Raziskane so različne strategije za zmanjševanje vpliva občutljivih spremenljivk ter uravnoteženje skupin v gručenju. Eksperimenti na sintetičnih in realnih podatkovnih množicah prikazujejo vpliv predlaganih pristopov na kakovost gručenja in pravičnost rezultatov. Rezultati kažejo, da je iskanje kompromisa med pravičnostjo in kakovostjo gručenja precejšnji izziv.
Keywords:strojno učenje, pravičnost, gručenje, občutljive spremenljivke, k-means
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[P. Praprotnik]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XI, 69 str.))
PID:20.500.12556/DKUM-95833 New window
UDC:004.85(043.2)
COBISS.SI-ID:266814467 New window
Publication date in DKUM:22.12.2025
Views:171
Downloads:26
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.10.2025

Secondary language

Language:English
Title:Fairness of the k-means clustering
Abstract:The thesis addresses the problem of fairness in the k-means clustering algorithm and its adapted variants. Various strategies are explored to reduce the influence of sensitive features and to balance groups within clusters. Experiments on synthetic and real-world datasets demonstrate the impact of the proposed approaches on clustering quality and fairness of results. The findings show that achieving a compromise between fairness and clustering quality represents a considerable challenge.
Keywords:machine learning, fairness, clustering, sensitive features, k-means


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