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Erschienen in:
Cluster Analysis and K-means Clustering: An Introduction Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

3. Generalizing Distance Functions for Fuzzy c-Means Clustering

verfasst von : Junjie Wu

Erschienen in: Advances in K-means Clustering

Verlag: Springer Berlin Heidelberg

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Abstract

Fuzzy \(c\)-means (FCM) is a well-known partitional clustering method, which allows an object to belong to two or more clusters with a membership grade between zero and one. Recently, due to the rich information conveyed by the membership grade matrix, FCM has been widely used in many real-world application domains where well-separated clusters are typically not available. In addition, people also recognize that the simple centroid-based iterative procedure of FCM is very appealing when dealing with large volume data.

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Vorheriges Kapitel The Uniform Effect of K-means Clustering
Nächstes Kapitel Information-Theoretic K-means for Text Clustering
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Metadaten
Titel
Generalizing Distance Functions for Fuzzy c-Means Clustering
verfasst von
Junjie Wu
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Advances in K-means Clustering

Print ISBN: 978-3-642-29806-6

Electronic ISBN: 978-3-642-29807-3

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-642-29807-3_3