Constructing fuzzy measures: A New method and its application to cluster analysis

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dc.contributor.author Yuan, Bo
dc.contributor.author Klir, George
dc.date.accessioned 2009-02-23T19:22:33Z
dc.date.available 2009-02-23T19:22:33Z
dc.date.issued 1996
dc.identifier.citation Bo Yuan and George Klir. Constructing fuzzy measures: A new method and its application in cluster analysis. In Proc. of NAFIPS'96, University of California at Berkeley, June 1996.
dc.identifier.uri http://hdl.handle.net/1850/8473
dc.description ©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.description.abstract In this paper, we first prove that for a given set of data there exists a fuzzy measure fitting exactly the data if and only if there exists an exact solution of the associated fuzzy relation equation. Secondly, we continue to study the special neural network we proposed in [6], and describe a learning algorithm for obtaining an approximate fuzzy measure when no one exactly fits the data. Finally, we propose a clustering method based on fuzzy measures and integrals. A benchmark data set, the well-known Iris data set, is adopted to illustrate the method.
dc.language.iso en_US
dc.publisher IEEE
dc.title Constructing fuzzy measures: A New method and its application to cluster analysis
dc.type Article

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