Random walk distances in data clustering and applications

Sijia Liu (), Anastasios Matzavinos () and Sunder Sethuraman ()

Advances in Data Analysis and Classification, 2013, vol. 7, issue 1, 83-108

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. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Spectral clustering; Fuzzy clustering methods; Random walks; Graph Laplacian; Mahalanobis; Face identification; 60J20; 62H30 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-013-0125-7

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