Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms h...

Descripción completa

Detalles Bibliográficos
Autores principales: Fong, Simon, Deb, Suash, Yang, Xin-She, Zhuang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151373/
https://www.ncbi.nlm.nih.gov/pubmed/25202730
http://dx.doi.org/10.1155/2014/564829

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151373/
https://www.ncbi.nlm.nih.gov/pubmed/25202730
http://dx.doi.org/10.1155/2014/564829

Ejemplares similares