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On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Consensus-Based Agglomerative Hierarchical Clustering

Authors : José Luis García-Lapresta, David Pérez-Román

Published in: Fuzzy Sets, Rough Sets, Multisets and Clustering

Publisher: Springer International Publishing

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Abstract

In this contribution, we consider that a set of agents assess a set of alternatives through numbers in the unit interval. In this setting, we introduce a measure that assigns a degree of consensus to each subset of agents with respect to every subset of alternatives. This consensus measure is defined as 1 minus the outcome generated by a symmetric aggregation function to the distances between the corresponding individual assessments. We establish some properties of the consensus measure, some of them depending on the used aggregation function. We also introduce an agglomerative hierarchical clustering procedure that is generated by similarity functions based on the previous consensus measures.

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previous chapter Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
next chapter Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach
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Metadata
Title
Consensus-Based Agglomerative Hierarchical Clustering
Authors
José Luis García-Lapresta
David Pérez-Román
Copyright Year
2017
Book
Fuzzy Sets, Rough Sets, Multisets and Clustering

Print ISBN: 978-3-319-47556-1

Electronic ISBN: 978-3-319-47557-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47557-8_8

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