Subspace clustering using affinity propagation

Guojun Gan*, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experiments on both synthetic data and real data show that the new algorithm outperforms the affinity propagation algorithm in recovering clusters from data.

Original languageEnglish
Pages (from-to)1455-1464
Number of pages10
JournalPattern Recognition
Volume48
Issue number4
DOIs
Publication statusPublished - 1 Apr 2015

Scopus Subject Areas

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

User-Defined Keywords

  • Affinity propagation
  • Attribute weighting
  • Data clustering
  • Subspace clustering

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