Interpreting multivariate membership degrees of fuzzy clustering methods: A strategy

Abstract : Fuzzy C-Means (FCM) is the most popular algorithm of the fuzzy clustering approach. Although FCM and its variations have shown good performance in cluster detection, they do not consider that different variables could produce different membership degrees. Motivated by this, the Multi-variate Fuzzy C-Means (MFCM) method was proposed. The MFCM computes membership degrees of both clusters and variables. However, up to now, there is no method that interprets the multivariate membership of the variables for aiding the understanding of clusters. The goal of this paper is, thus, to bridge this gap by proposing a method to do so. In order to illustrate the method proposed, experiments with a synthetic dataset and applications concerning cancer gene expression data are carried out.
Type de document :
Communication dans un congrès
Proc. of the 2017 International Joint Conference on Neural Networks (IJCNN), May 2017, Anchorage, United States. IEEE, Proc. of the 2017 International Joint Conference on Neural Networks (IJCNN), pp.2800-2804, 2017, 〈10.1109/IJCNN.2017.7966201〉
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https://hal.archives-ouvertes.fr/hal-01562009
Contributeur : Marcilio De Souto <>
Soumis le : jeudi 13 juillet 2017 - 14:41:10
Dernière modification le : mercredi 16 mai 2018 - 12:14:01

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Bruno Pimentel, Marcilio De Souto, Renata De Souza. Interpreting multivariate membership degrees of fuzzy clustering methods: A strategy. Proc. of the 2017 International Joint Conference on Neural Networks (IJCNN), May 2017, Anchorage, United States. IEEE, Proc. of the 2017 International Joint Conference on Neural Networks (IJCNN), pp.2800-2804, 2017, 〈10.1109/IJCNN.2017.7966201〉. 〈hal-01562009〉

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