Subspace clustering with automatic feature grouping

Guojun Gan*, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

37 Citations (Scopus)


This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG-k-means algorithm in terms of accuracy and choice of parameters.

Original languageEnglish
Pages (from-to)3703-3713
Number of pages11
JournalPattern Recognition
Issue number11
Publication statusPublished - 1 Nov 2015

Scopus Subject Areas

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

User-Defined Keywords

  • Data clustering
  • Feature group
  • k-means
  • Subspace clustering


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