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Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

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

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.

Keywords

  • Fuzzy clustering
  • Clustering visualization
  • Membership network
  • High-dimensional data

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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.

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Correspondence to Leandro Ariza-Jiménez .

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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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  • Online ISBN: 978-3-030-31019-6

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