Optimization of fuzzy clustering criteria using genetic algorithms
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 589-594
- https://doi.org/10.1109/icec.1994.349993
Abstract
This paper introduces a general approach based on genetic algorithms for optimizing a broad class of clustering criteria. The standard approach for optimizing these criteria has been to alternate optimizations between the variables which represent fuzzy memberships of the data to various clusters, and those prototype variables which determine the geometry of the clusters. The approach suggested here first re-parameterizes the criteria into functions of the prototype variables alone. The prototype variables are then coded as binary strings so that genetic algorithms can be applied. An overview of the approach and two simple numerical examples are given.<>Keywords
This publication has 5 references indexed in Scilit:
- Switching regression models and fuzzy clusteringIEEE Transactions on Fuzzy Systems, 1993
- The fuzzy c spherical shells algorithm: A new approachIEEE Transactions on Neural Networks, 1992
- Adaptive fuzzy c-shells clustering and detection of ellipsesIEEE Transactions on Neural Networks, 1992
- Grouped coordinate minimization using Newton's method for inexact minimization in one vector coordinateJournal of Optimization Theory and Applications, 1991
- Pattern Recognition with Fuzzy Objective Function AlgorithmsPublished by Springer Nature ,1981