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A Multi-level Thresholding-Based Method to Learn Fuzzy Membership Functions from Data Warehouse

  • Dario Rojas
  • Carolina Zambrano
  • Marcela Varas
  • Angelica Urrutia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

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.

Keywords

Fuzzy Logic Data Warehouse Multi-Level Thresholding Clustering Clustering Validation Indices 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dario Rojas
    • 1
  • Carolina Zambrano
    • 1
  • Marcela Varas
    • 2
  • Angelica Urrutia
    • 3
  1. 1.Depto. de Ingeniería Informática y Ciencias de la ComputaciónUniversidad de AtacamaCopiapóChile
  2. 2.Depto. de Ingeniería Informática y Ciencias de la ComputaciónUniversidad de ConcepciónConcepciónChile
  3. 3.Depto. de Computación e InformáticaUniversidad Católica del MauleTalcaChile

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