A Multivariate Fuzzy Kohonen Clustering Network

• Published in: 2019 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 7
• Main Authors: Cavalcanti, Rodrigo B. de C., Pimentel, Bruno A., de Almeida, Carlos W.D., de Souza, Renata M.C.R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2019
• Subjects: Clustering algorithms; Data models; Indexes; Linear programming; Machine learning algorithms; Monte Carlo methods; Neural networks
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2019.8852243

Usually, in a fuzzy clustering, the memberships are the same for all the variables (features), i.e., the variables are considered equally important for the definition of the memberships. Fuzzy Kohonen Clustering network (FKCN) is a self-organizing fuzzy neural network that uses fuzzy membership values from the popular Fuzzy c-Means as learning rates. The replacement of the arbitrary learning rate by a fuzzy membership function can produce better clustering results. This paper introduces a new variant of the FKCN algorithm that finds a set of weights and a multivariate fuzzy partition minimizing an objective function. Here, the multivariate memberships allow to take account the intra-class and inter-class dispersion structures of the input data. Experiments with different configurations of synthetic data sets and applications with real data sets demonstrate the usefulness of this fuzzy clustering network model.