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Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

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Applied Computer Sciences in Engineering (WEA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1052))

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Abstract

Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank.

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Acknowledgements

This research work was supported by Centro de Excelencia y Apropiación en Big Data y Data Analytics -Alianza CAOBA- and Universidad EAFIT.

Author information

Authors and Affiliations

  1. Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombia

    Leandro Ariza-Jiménez & Olga Lucía Quintero

  2. System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombia

    Luisa F. Villa

Authors
  1. Leandro Ariza-Jiménez
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  2. Luisa F. Villa
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  3. Olga Lucía Quintero
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Corresponding author

Correspondence to Leandro Ariza-Jiménez .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Antonio Nariño, Bogotá, Colombia

    Mario Duarte-González

  3. Universidad Antonio Nariño, Bogotá, Colombia

    Sebastián Jaramillo-Isaza

  4. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañon

  5. Corporación Unificada Nacional CUN, Bogotá, Colombia

    Yesid Díaz-Gutierrez

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Ariza-Jiménez, L., Villa, L.F., Quintero, O.L. (2019). Memberships Networks for High-Dimensional Fuzzy Clustering Visualization. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_23

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  • DOI: https://doi.org/10.1007/978-3-030-31019-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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