Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms

The K-means algorithm groups datasets into different groups, defines a fixed number of clusters, iteratively assigning data to the clusters formed by adjusting the centers in each cluster. K-means algorithm uses an unsupervised learning method to discover patterns in an input data set. The purpose of the research is to propose a municipal management classification model in the municipalities of Peru using a K-means clustering algorithm based in 58 variables obtained from the areas of human resources, heavy machinery and operating vehicles, information and communication technologies, municipal planning, municipal finances, local economic development, social services, solid waste management, cultural, recreational and sports facilities, public security, disaster risk management, environmental protection and conservation of all the municipalities of the 24 departments of Peru and the constitutional province of Callao. The results of the application of the K-means algorithm show that 32% of the municipalities made up of the municipal governments of Amazonas, Apurímac, Huancavelica, Huánuco, Ica, Lambayeque, Loreto and San Martin; are in Cluster 1; the 8% in Cluster 2 with the municipal governments of Ancash and Cusco; in the third Cluster the 28% with the municipal governments of the constitutional Province of Callao, Madre de Dios, Moquegua, Pasco, Tacna, Tumbes and Ucayali and in Cluster 4, 32% composed of the municipal governments of Arequipa, Ayacucho, Cajamarca, Junín, La Libertad, Lima, Piura and Puno Region.

@article{Morales2020,
  title     = {Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Jose Morales and Nakaday Vargas and Mario Coyla and Jose Huanca},
  doi       = {10.14569/IJACSA.2020.0110770},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110770}
}