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EVALUATION OF INITIALIZATION METHODS FOR THE PERFORMANCE OF THE K-MEANS ALGORITHM

MAY 2025   -  Volume: 100 -  Pages: 204-210

DOI:

https://doi.org/10.52152/D11325

Authors:

SINUHE GINES PALESTINO -
EDUARDO ROLDAN REYES
- MARCELA QUIROZ CASTELLANOS - GUILLERMO CORTES ROBLES

Disciplines:

  • Telecommunications technology (INTELIGENCIA ARTIFICIAL )

Downloads:   33

How to cite this paper:  
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Key words:
K-means, heuristic, clustering, performance, initialization, algorithm, centroids, metaheuristic, accuracy, evaluation, methods, applications.
Article type:
COLABORACION/COLLABORATION DAMR
Section:
RESEARCH ARTICLES

The K-means algorithm is one of the most widely used unsupervised machine learning methods; it helps to sort data clusters into a given number of groups with a pattern association that identifies relevant information in research domains. The heuristics of the algorithm are adaptable and easy to implement; however, one of its most notorious weaknesses is the poor assignment of K groups. This paper aims to analyze the different means of initialization and performance of the algorithm, as well as some applications of K-means in different industry sectors through a literature review, addressing relevant aspects to conclude in which cases transcendent results are obtained.

Keywords: K-means, heuristic, clustering, performance, initialization, algorithm, centroids, metaheuristic, accuracy, evaluation, methods, applications.

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