Optimization of k-means with simulated annealing heuristics

• Autores: Ivan O. Cruz Ruiz, Pedro Lara Velázquez, Eric Alfredo Rincón García, Miguel Angel Gutiérrez Andrade, Sergio Gerardo de los Cobos Silva, Román Anselmo Mora Gutiérrez, Carlos A. Hernández Nava
• Localización: Fuzzy economic review, ISSN 1136-0593, Vol. 26, Nº. 2, 2021
• Idioma: inglés
• DOI: 10.25102/fer.2021.02.03
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• Resumen

  • The classification algorithm known as k-means is widely used in different branches of artificial intelligence. Its main objective is to find the k centroids that can repre-sent clusters of information. While this algorithm is reliable, it is not always possi-ble to find the optimal centroids that represent the data set. In this work, different metaheuristic techniques were applied in which the problem of finding the initial centroids was solved, and a novel hybrid algorithm is presented that finds better centroids than those found by the standard k-means algorithm. The new approach improves the classification of the information contained in the tested databases.