 Applying the Gradient Projection Method to a Model of Proportional Membership for Fuzzy Cluster AnalysisSusana Nascimento ()
A chapter in Optimization and Its Applications in Control and Data Sciences, 2016, pp 353-380 from Springer Abstract: 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. Keywords: Fuzzy proportional membership; Gradient projection method; Extremal cluster prototype (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-42056-1_13 Ordering information: This item can be ordered from DOI: 10.1007/978-3-319-42056-1_13 Access Statistics for this chapter More chapters in Springer Optimization and Its Applications from Springer |
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