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FUZZY ECONOMIC REVIEW

ISSN (print) 1136-0593 · ISSN (online) 2445-4192

OPTIMIZATION OF K-MEANS WITH SIMULATED ANNEALING HEURISTICS

Ivan O. Cruz-Ruiz. Universidad Autónoma Metropolitana Unidad Iztapalapa, Av. San Rafael Atlixco 186, 09340, Ciudad de México, México. Email: iocr@xanum.uam.mx

Pedro Lara-Velázquez. Universidad Nacional Autónoma de México, C.U., Coyoacán, 04510, Ciudad de México, México. plara@xanum.uam.mx

Eric A. Rincón-García. Universidad Nacional Autónoma de México, C.U., Coyoacán, 04510, Ciudad de México, México. rincon@xanum.uam.mx

Miguel A. Gutiérrez-Andrade. Universidad Nacional Autónoma de México, C.U., Coyoacán, 04510, Ciudad de México, México. gamma@xanum.uam.mx

Sergio G. de-los-Cobos-Silva. Universidad Nacional Autónoma de México, C.U., Coyoacán, 04510, Ciudad de México, México. cobos@xanum.uam.mx

Roman A. Mora-Gutiérrez. Universidad Autónoma Metropolitana Unidad Azcapotzalco, Av. San Pablo Xalpa 180, 02200, Ciudad de México, México. mgra@correo.azc.uam.mx

Carlos A. Hernández-Nava. Universidad Autónoma Metropolitana Unidad Iztapalapa, Av. San Rafael Atlixco 186, 09340, Ciudad de México, México. Email: cahn@xanum.uam.mx

Abstract

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.

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