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Erschienen in:
On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

On Some Clustering Algorithms Based on Tolerance

verfasst von : Yukihiro Hamasuna, Yasunori Endo

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

Verlag: Springer International Publishing

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Abstract

A large number of clustering algorithms have been proposed to handle target data and deal with various real-world problems such as uncertain data mining, semi-supervised learning and so on. We focus above two topics and introduce two concepts to construct significant clustering algorithms. We propose tolerance and penalty-vector concepts for handling uncertain data. We also propose clusterwise tolerance concept for semi-supervised learning. These concepts are quite similar approach in the viewpoint of handling objects to be flexible to each clustering topics. We construct two clustering algorithms FCMT and FCMQ for handling uncertain data. We also construct two clustering algorithms FCMCT and SSFCMCT for semi-supervised learning. We consider that those concepts have a potential to resolve conventional and brand new clustering topics in various ways.

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Vorheriges Kapitel Various Types of Objective-Based Rough Clustering
Nächstes Kapitel Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
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Metadaten
Titel
On Some Clustering Algorithms Based on Tolerance
verfasst von
Yukihiro Hamasuna
Yasunori Endo
Copyright-Jahr
2017
Buch
Fuzzy Sets, Rough Sets, Multisets and Clustering

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

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

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-47557-8_6