Automatic design of interpretable fuzzy predicate systems for clustering using self-organizing maps

• Autores: Meschino, Gustavo Javier; Comas, Diego Sebastián; Ballarin, Virginia Laura; Scandurra, Adriana Gabriela; Passoni, Lucía Isabel
• Año de publicación: 2015
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Clustering; Degree of Truth; Fuzzy Logic; Fuzzy Predicates; Self-Organizing Maps
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

In the area of pattern recognition, clustering algorithms are a family of unsupervised classifiers designed with the aim to discover unrevealed structures in the data. While this is a never ending research topic, many methods have been developed with good theoretical and practical properties. One of such methods is based on self organizing maps (SOM), which have been successfully used for data clustering, using a two levels clustering approach. Newer on the field, clustering systems based on fuzzy logic improve the performance of traditional approaches. In this paper we combine both approaches. Most of the previous works on fuzzy clustering are based on fuzzy inference systems, but we propose the design of a new clustering system in which we use predicate fuzzy logic to perform the clustering task, being automatically designed based on data. Given a datum, degrees of truth of fuzzy predicates associated with each cluster are computed using continuous membership functions defined over data features. The predicate with the maximum degree of truth determines the cluster to be assigned. Knowledge is discovered from data, obtained using the SOM generalization aptitude and taking advantage of the well-known SOM abilities to discover natural data grouping when compared with direct clustering. In addition, the proposed approach adds linguistic interpretability when membership functions are analyzed by a field expert. We also present how this approach can be used to deal with partitioned data. Results show that clustering accuracy obtained is high and it outperforms other methods in the majority of datasets tested.
Fil: Meschino, Gustavo Javier. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina
Fil: Comas, Diego Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Scandurra, Adriana Gabriela. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Passoni, Lucía Isabel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina