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International Congress of Telematics and Computing

WITCOM 2019: Telematics and Computing pp 145–161Cite as

Clustering Methodology in Mixed Data Sets

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1053))

Abstract

One of the most challenging tasks of data analysis is finding clusters in mixed data sets, as they have numerical and categorical variables, and lack a labeled variable to serve as a guide. These clusters could serve to summarize all the variables of a data set into one and be able to find information more easily than generating summarizations for each variable. In this research thesis, a methodology of clustering on mixed data sets is proposed, which yields better results than the methods applied in the state of the art.

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Author information

Authors and Affiliations

  1. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Avenida Instituto Politécnico Nacional No. 2580, Col Barrio la Laguna Ticomán, Gustavo A. Madero, 07340, Mexico City, Mexico

    Jacobo Gerardo González León & Miguel Félix Mata Rivera

Authors
  1. Jacobo Gerardo González León
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  2. Miguel Félix Mata Rivera
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Corresponding authors

Correspondence to Jacobo Gerardo González León or Miguel Félix Mata Rivera .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Miguel Felix Mata-Rivera

  2. Instituto Politécnico Nacional, Mexico City, Mexico

    Roberto Zagal-Flores

  3. Universidad Mayor, Santiago, Chile

    Cristian Barría-Huidobro

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González León, J.G., Mata Rivera, M.F. (2019). Clustering Methodology in Mixed Data Sets. In: Mata-Rivera, M., Zagal-Flores, R., Barría-Huidobro, C. (eds) Telematics and Computing. WITCOM 2019. Communications in Computer and Information Science, vol 1053. Springer, Cham. https://doi.org/10.1007/978-3-030-33229-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-33229-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33228-0

  • Online ISBN: 978-3-030-33229-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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