Density based fuzzy membership functions in the context of geocomputation

Victor Lobo, Fernando Bação, Miguel Loureiro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Geocomputation has a long tradition in dealing with fuzzyness in different contexts, most notably in the challenges created by the representation of geographic space in digital form. Geocomputation tools should be able to address the imminent continuous nature of geo phenomena, and its accompanying fuzzyness. Fuzzy Set Theory allows partial memberships of entities to concepts with non-crisp boundaries. In general, the application of fuzzy methods is distance-based and for that reason is insensible to changes in density. In this paper a new method for defining density-based fuzzy membership functions is proposed. The method automatically determines fuzzy membership coefficients based on the distribution density of data. The density estimation is done using a Self-Organizing Map (SOM). The proposed method can be used to accurately describe clusters of data which are not well characterized using distance methods. We show the advantage of the proposed method over traditional distance-based membership functions.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
Number of pages8
Volume4488 LNCS
EditionPART 2
Publication statusPublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: 27 May 200730 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4488 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Computational Science, ICCS 2007


  • Density based clustering
  • Fuzzy membership
  • Fuzzy set theory
  • SOM


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