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

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Optimization and Its Applications in Control and Data Sciences

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 115))

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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Notes

  1. 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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Acknowledgements

I wish to thank Professor Boris Mirkin for having introduce me to fuzzy clustering within the Data Recovery paradigm, and for all the years of discussion that we have been sharing ever since. The comments from the anonymous Reviewers are greatly acknowledged as they contributed to improve the paper.

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  1. Department of Computer Science and NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Susana Nascimento

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  1. Susana Nascimento
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Correspondence to Susana Nascimento .

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  1. Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA

    Boris Goldengorin

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Nascimento, S. (2016). Applying the Gradient Projection Method to a Model of Proportional Membership for Fuzzy Cluster Analysis. In: Goldengorin, B. (eds) Optimization and Its Applications in Control and Data Sciences. Springer Optimization and Its Applications, vol 115. Springer, Cham. https://doi.org/10.1007/978-3-319-42056-1_13

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