Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Random selection of initial centroids (centers) for clusters is a fundamental defect in K-means clustering algorithm as the algorithm's performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in K-means c...

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Detalles Bibliográficos
Autores principales: Pourahmad, Saeedeh, Basirat, Atefeh, Rahimi, Amir, Doostfatemeh, Marziyeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416251/
https://www.ncbi.nlm.nih.gov/pubmed/32802153
http://dx.doi.org/10.1155/2020/7636857

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416251/
https://www.ncbi.nlm.nih.gov/pubmed/32802153
http://dx.doi.org/10.1155/2020/7636857

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