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

Jose Morales,Jose Huanca,Nakaday Vargas,Mario Coyla

doi:10.14569/ijacsa.2020.0110770

Abstract

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.

Highlights

The clustering problem is one of the most studied topics in the data mining and machine learning communities
The purpose of the research is to propose a classification model for municipal management of local governments in Peru based on K-means clustering algorithms, at the same time evaluating the characteristics of local governments in every cluster
Model type: Clustering Fig. 1 shows the K-means algorithm used to establish the classification of municipal management in local governments in Peru

Summary

Introduction

The clustering problem is one of the most studied topics in the data mining and machine learning communities. Grouping is a diverse topic, and the underlying algorithms are highly dependent on the data domain and the scenario in which the problems occur. The objective of clustering is to classify a set of elements into groups that are very similar among them, but different with elements from other groups. Author in [1] consider the k-Means grouping algorithm, to be one of the most efficient grouping algorithms for large-scale data sets. The K-means algorithm allows clustering by grouping objects into k groups, this is why it becomes very important for researchers and so its results, which will be used in the municipalities of Peru that promote local development, and adapting themselves to current organizations’ real situation; mainly with the objective to improve the provision of local public services; it will allow a continuous improvement in the offered services

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Conclusion