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Title:Implementacija k-means gručenja z genetskim algoritmom
Authors:ID Šaruga, Alen (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
ID Fister, Iztok (Comentor)
Files:.pdf VS_Saruga_Alen_2024.pdf (1,34 MB)
MD5: 0058A7884EEA2DE0F2FAC15B8F04B6FA
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:K-means algoritem je eden najpopularnejših in najučinkovitejših algoritmov gručenja podatkov. Kljub temu algoritem predstavlja izziv, saj je občutljiv na začetno postavitev centroidov gruč. Zato lahko algoritem stremi k lokalnemu optimumu in ne h globalno optimalni rešitvi. Namen diplomskega dela je implementacija optimiziranega k-means algoritma, manj občutljivega na začetne centroide gruč, z uporabo genetskega algoritma. Delo se osredotoča na postopek gručenja in genetski algoritem. Implementacija je izvedena v programskem jeziku Python s knjižnico NiaPy. Na koncu so predstavljeni rezultati eksperimentov, kjer je izvedena primerjava standardnega in optimiziranega k-means algoritma na različnih podatkovnih množicah.
Keywords:gručenje, k-means, genetski algoritem, centroidi
Place of publishing:Maribor
Publisher:[A. Šaruga]
Year of publishing:2024
PID:20.500.12556/DKUM-89496 New window
UDC:004.421.2:004.627(043.2)
COBISS.SI-ID:220095747 New window
Publication date in DKUM:19.09.2024
Views:157
Downloads:69
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:15.07.2024

Secondary language

Language:English
Title:Implementing k-means clustering with a genetic algorithm
Abstract:The k-means algorithm is one of the most popular and effective clustering algorithms. However, it presents a challenge as it is sensitive to the initial placement of cluster centroids. Therefore, the algorithm can converge to a local optimum rather than a globally optimal solution. The purpose of this thesis is to implement an optimized k-means algorithm that is less sensitive to the initial cluster centroid using a genetic algorithm. The work focuses on the clustering process and the genetic algorithm. The implementation will be carried out in the Python programming language using the NiaPy library. Finally, the results of the experiments comparing the standard k-means and the optimized k-means algorithm on various datasets will be presented.
Keywords:clustering, k-means, genetic algorithm, centroids


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