Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

Leandro Ariza-Jiménez, Luisa F. Villa, Olga Lucía Quintero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaApplied Computer Sciences in Engineering - 6th Workshop on Engineering Applications, WEA 2019, Proceedings
EditoresJuan Carlos Figueroa-García, Mario Duarte-González, Sebastián Jaramillo-Isaza, Alvaro David Orjuela-Cañon, Yesid Díaz-Gutierrez
EditorialSpringer Heidelberg
Número de páginas11
ISBN (versión impresa)9783030310189
EstadoPublicada - 1 ene 2019
Evento6th Workshop on Engineering Applications, WEA 2019 - Santa Marta, Colombia
Duración: 16 oct 201918 oct 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia6th Workshop on Engineering Applications, WEA 2019
CiudadSanta Marta


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