A spectral approach to clustering numerical vectors as nodes in a network

Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


We address the issue of clustering examples by integrating multiple data sources, particularly numerical vectors and nodes in a network. We propose a new, efficient spectral approach, which integrates the two costs for clustering numerical vectors and clustering nodes in a network into a matrix trace, reducing the issue to a trace optimization problem which can be solved by an eigenvalue decomposition. We empirically demonstrate the performance of the proposed approach through a variety of experiments, including both synthetic and real biological datasets.

Original languageEnglish
Pages (from-to)236-251
Number of pages16
JournalPattern Recognition
Issue number2
Publication statusPublished - 2011 Feb
Externally publishedYes


  • Data integration
  • Heterogeneous data
  • Semi-supervised clustering
  • Spectral clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'A spectral approach to clustering numerical vectors as nodes in a network'. Together they form a unique fingerprint.

Cite this