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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 1/2013

01.03.2013 | Regular Article

Random walk distances in data clustering and applications

verfasst von: Sijia Liu, Anastasios Matzavinos, Sunder Sethuraman

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2013

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Abstract

In this paper, we develop a family of data clustering algorithms that combine the strengths of existing spectral approaches to clustering with various desirable properties of fuzzy methods. In particular, we show that the developed method “Fuzzy-RW,” outperforms other frequently used algorithms in data sets with different geometries. As applications, we discuss data clustering of biological and face recognition benchmarks such as the IRIS and YALE face data sets.
Vorheriger Artikel Functional fuzzy clusterwise regression analysis
Nächster Artikel Regularized logistic discrimination with basis expansions for the early detection of Alzheimer’s disease based on three-dimensional MRI data

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Fußnoten
1
For further discussion of the emerging role of data geometry in the development of data clustering algorithms (see, e.g., Chen and Lerman 2009; Haralick and Harpaz 2007; Coifman and Lafon 2006).
 
2
As it is usually the case in data clustering applications, the employed distance measures do not have to necessarily satisfy the properties of a metric (see, e.g., Chen and Zhang 2004).
 
3
Data clustering on linear manifolds, or affine spaces was first introduced by Bock (1974). Adopting the terminology of Haralick and Harpaz (2007), we say that \(L\) is a linear manifold in a vector space \(V\) if for some vector subspace \(S\) of \(V\) and some translation \(t\in V,\,L=\{t+s \mid s\in S\}.\)
 
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Metadaten
Titel
Random walk distances in data clustering and applications
verfasst von
Sijia Liu
Anastasios Matzavinos
Sunder Sethuraman
Publikationsdatum
01.03.2013
Verlag
Springer-Verlag
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 1/2013
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-013-0125-7

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