A multi-level thresholding-based method to learn fuzzy membership functions from data warehouse

Authors:
Dario Rojas;Carolina Zambrano;Marcela Varas;Angelica Urrutia
Affiliations:
Depto. de Ingeniería Informática y Ciencias de la Computación, Universidad de Atacama, Copiapó, Chile;Depto. de Ingeniería Informática y Ciencias de la Computación, Universidad de Atacama, Copiapó, Chile;Depto. de Ingeniería Informática y Ciencias de la Computación, Universidad de Concepción, Concepción, Chile;Depto. de Computación e Informática, Universidad Católica del Maule, Talca, Chile
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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Abstract

Learn fuzzy membership functions automatically for characterization and operation of fuzzy measures in Data Warehouse is a problem of recent concern. This paper presents a new method to learn membership functions of linguistic labels of fuzzy measures from Data Warehouse. We proposed a multilevel thresholding based method with clustering validation indices in order to obtain optimal number of labels and parameters of membership functions. Validation is performed by comparing the proposal against a supervised learning approach based on clustering and genetic algorithms, including the application in response to queries in a Data Warehouse with fuzzy measures.