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

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Title: Constructing fuzzy measures: A New method and its application to cluster analysis
Author: Yuan, Bo; Klir, George
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.
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.
Record URI: http://hdl.handle.net/1850/8473
Date: 1996

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