Data Clustering and Graph Partitioning via Simulated Mixing

Shahzad Bhatti, Carolyn Beck, Angelia Nedich

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the recent past to accelerate spectral clustering, however they compromise on the accuracy of the spectral clustering to achieve faster speed. In this paper, we propose a novel spectral clustering algorithm based on a mixing process on a graph. Unlike the existing spectral clustering algorithms, our algorithm does not require computing eigenvectors. Specifically, it finds the equivalent of a linear combination of eigenvectors of the normalized similarity matrix weighted with corresponding eigenvalues. This linear combination is then used to partition the dataset into meaningful clusters. Simulations on real datasets show that partitioning datasets based on such linear combinations of eigenvectors achieves better accuracy than standard spectral clustering methods as the number of clusters increase. Our algorithm can easily be implemented in a distributed setting.

Original languageEnglish (US)
JournalIEEE Transactions on Network Science and Engineering
StateAccepted/In press - Apr 3 2018


  • Approximation algorithms
  • Atmospheric measurements
  • Clustering algorithms
  • Clustering algorithms
  • community detection
  • graph partitioning
  • Machine learning algorithms
  • Markov processes
  • Partitioning algorithms
  • random walk
  • Signal processing algorithms

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

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