Charla – Nash equilibrium-based K-means Algorithm

Este lunes 28 de julio a las 15:30 h, en el salón multifuncional, el Prof. Mehdi Salimi  dictará una charla titulada:

Nash equilibrium-based K-means Algorithm

------------------

Abstract:

A key method in unsupervised machine learning is clustering, which groups

related data points according to preset standards. Despite its widespread use, the conventional K-means method has a number of drawbacks, such as its sensitivity to initial cluster centers and its exclusive dependence on distance metrics. The study presents the K-means clustering algorithm by integrating the Nash equilibrium, a Game Theory concept, to enhance clustering accuracy. Clustering performance is enhanced by the suggested method, which allows cluster centers to dynamically modify their locations in response to competition. 

Por mas información comunicarse con diego.armentano@fcea.edu.uy.