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Analyzing Electoral Data Using Partitional and Hierarchical Clustering Algorithms

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 646))

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Abstract

This paper describes the use of two clustering algorithms for investigating how results from the second round of the last Brazilian presidential election in Brazil are organized, taking into account values of the Municipal Human Development Index (MHDI). The investigation intended to uncover possible relationships between indicators that characterize profiles of Brazilian voters and profiles of Brazilian municipalities. MHDI is a customized version of the Human Development Index (HDI), and represents the development and quality of life offered by Brazilian municipalities. The work carried out is based on data results of the last presidential elections in Brazil, held in 2018, focusing on municipality´s data described by MHDI sub-indexes, as well as municipality’s population, related to the 5,558 municipalities in the country. The analysis of results of the second turn of the last presidential election, taking into account MHDI values was carried out based on clustering results induced by two clustering algorithms that employ different strategies: hierarchical (algorithm DIANA) and partitional (algorithm k-Means).

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Acknowledgments

Authors thank CAPES, CNPq and UNIFACCAMP.

Author information

Authors and Affiliations

  1. Faculdade Campo Limpo Paulista (FACCAMP), C. L. Paulista, SP, Brazil

    Paulo Rogerio Nietto, Maria do Carmo Nicoletti & Nilton Cesar Sacco

  2. Universidade Federal de São Carlos (UFSCar), S. Carlos, SP, Brazil

    Maria do Carmo Nicoletti

  3. Faculdade de Tecnologia (FATEC), Araras, SP, Brazil

    Nilton Cesar Sacco

Authors
  1. Paulo Rogerio Nietto
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  2. Maria do Carmo Nicoletti
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  3. Nilton Cesar Sacco
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Corresponding author

Correspondence to Paulo Rogerio Nietto .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Nietto, P.R., do Carmo Nicoletti, M., Sacco, N.C. (2023). Analyzing Electoral Data Using Partitional and Hierarchical Clustering Algorithms. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_6

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