Random walk distances in data clustering and applications

Sijia Liu, Anastasios Matzavinos, Sunder Sethuraman

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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 languageEnglish (US)
Pages (from-to)83-108
Number of pages26
JournalAdvances in Data Analysis and Classification
Issue number1
StatePublished - Mar 2013


  • 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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