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2012 | OriginalPaper | Chapter

4. Incremental Spectral Clustering

Authors : Abdelhamid Bouchachia, Markus Prossegger

Published in: Learning in Non-Stationary Environments

Publisher: Springer New York

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Abstract

In the present contribution, a novel algorithm for off-line spectral clustering algorithm is introduced and an online extension is derived in order to deal with sequential data. The proposed algorithm aims at dealing with nonconvex clusters having different forms. It relies on the notion of communicability that allows to handle the contiguity of data distribution. In the second part of the paper, an incremental extension of the fuzzy c-varieties is proposed to serve as a building block of the incremental spectral clustering algorithm (ISC). Initial simulations are presented towards the end of the contribution to show the performance of the ISC algorithm.

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previous chapter A Granular Description of Data: A Study in Evolvable Systems
next chapter Semisupervised Dynamic Fuzzy K-Nearest Neighbors
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Metadata
Title
Incremental Spectral Clustering
Authors
Abdelhamid Bouchachia
Markus Prossegger
Copyright Year
2012
Publisher
Springer New York
Book
Learning in Non-Stationary Environments

Print ISBN: 978-1-4419-8019-9

Electronic ISBN: 978-1-4419-8020-5

Copyright Year: 2012

DOI
https://doi.org/10.1007/978-1-4419-8020-5_4

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