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A New Adaptive Conjugate Gradient Algorithm for Large-Scale Unconstrained Optimization Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Applying the Gradient Projection Method to a Model of Proportional Membership for Fuzzy Cluster Analysis

Author : Susana Nascimento

Published in: Optimization and Its Applications in Control and Data Sciences

Publisher: Springer International Publishing

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Abstract

This paper presents a fuzzy proportional membership model for clustering (FCPM). Unlike the other clustering models, FCPM requires that each entity may express an extent of each prototype, which makes its criterion to loose the conventional prototype-additive structure. The methods for fitting the model at different fuzziness parameter values are presented. Because of the complexity of the clustering criterion, minimization of the errors requires the gradient projection method (GPM). We discuss how to find the projection of a vector on the simplex of the fuzzy membership vectors and how the stepsize length of the GPM had been fixed. The properties of the clusters found with the FCPM are discussed. Especially appealing seems the property to keep the extremal cluster prototypes stable even after addition of many entities around the grand mean.

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previous chapter Extremal Results for Algebraic Linear Interval Systems
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Footnotes
1
In the follow up text the number of clusters is denoted by K instead of c as in the FCM.
 
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Metadata
Title
Applying the Gradient Projection Method to a Model of Proportional Membership for Fuzzy Cluster Analysis
Author
Susana Nascimento
Copyright Year
2016
Book
Optimization and Its Applications in Control and Data Sciences

Print ISBN: 978-3-319-42054-7

Electronic ISBN: 978-3-319-42056-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42056-1_13