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