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

2017 | OriginalPaper | Chapter

On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data

Authors : Laya Aliahmadipour, Vicenç Torra, Esfandiar Eslami

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

Publisher: Springer International Publishing

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Abstract

Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and initial parametrization of a clustering (classification) algorithm. Hesitant fuzzy set theory is a suitable tool to deal with this kind of problems. In this study, we introduce two different points of view to apply hesitant fuzzy sets in the data mining tasks, specially in the clustering algorithms.

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previous chapter Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach
next chapter Experiences Using Decision Trees for Knowledge Discovery
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Metadata
Title
On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
Authors
Laya Aliahmadipour
Vicenç Torra
Esfandiar Eslami
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_10

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