Random walk distances in data clustering and applications

Sijia Liu; Anastasios Matzavinos; Sunder Sethuraman

doi:10.1007/s11634-013-0125-7

Random walk distances in data clustering and applications

Sijia Liu, Anastasios Matzavinos, Sunder Sethuraman

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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.

Original language	English (US)
Pages (from-to)	83-108
Number of pages	26
Journal	Advances in Data Analysis and Classification
Volume	7
Issue number	1
DOIs	https://doi.org/10.1007/s11634-013-0125-7
State	Published - Mar 2013
Externally published	Yes

Keywords

Face identification
Fuzzy clustering methods
Graph Laplacian
Mahalanobis
Random walks
Spectral clustering

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications

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10.1007/s11634-013-0125-7

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keywords = "Face identification, Fuzzy clustering methods, Graph Laplacian, Mahalanobis, Random walks, Spectral clustering",

author = "Sijia Liu and Anastasios Matzavinos and Sunder Sethuraman",

note = "Funding Information: Acknowledgments The research of SL has been supported in part by an Alberta Wolfe Research Fellowship from the Iowa State University Mathematics department. The research of AM has been supported in part by the Mathematical Biosciences Institute and the National Science Foundation under Grant DMS 0931642. The research of SS is supported in part by NSF DMS-1159026.",

year = "2013",

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doi = "10.1007/s11634-013-0125-7",

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AU - Liu, Sijia

AU - Matzavinos, Anastasios

AU - Sethuraman, Sunder

N1 - Funding Information: Acknowledgments The research of SL has been supported in part by an Alberta Wolfe Research Fellowship from the Iowa State University Mathematics department. The research of AM has been supported in part by the Mathematical Biosciences Institute and the National Science Foundation under Grant DMS 0931642. The research of SS is supported in part by NSF DMS-1159026.

PY - 2013/3

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N2 - 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.

AB - 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.

KW - Face identification

KW - Fuzzy clustering methods

KW - Graph Laplacian

KW - Mahalanobis

KW - Random walks

KW - Spectral clustering

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Random walk distances in data clustering and applications

Abstract

Keywords

ASJC Scopus subject areas

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