Interpreting multivariate membership degrees of fuzzy clustering methods: A strategy

• Published in: 2017 International Joint Conference on Neural Networks (IJCNN) pp. 2800 - 2804
• Main Authors: Pimentel, Bruno A., de Souto, Marcilio C. P., de Souza, Renata M. C. R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2017
• Subjects: Cancer; Clustering algorithms; Clustering methods; Gene expression; Partitioning algorithms; Prototypes
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2017.7966201

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.