QUAC: Quick unsupervised anisotropic clustering

D. Hanwell*, M. Mirmehdi

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

We present a novel unsupervised algorithm for quickly finding clusters in multi-dimensional data. It does not make the assumption of isotropy, instead taking full advantage of the anisotropic Gaussian kernel, to adapt to local data shape and scale. We employ some little-used properties of the multivariate Gaussian distribution to represent the data, and also give, as a corollary of the theory we formulate, a simple yet principled means of preventing singularities in Gaussian models. The efficacy and robustness of the proposed method are demonstrated on both real and artificial data, providing qualitative and quantitative results, and comparing against the well known mean-shift and K-means algorithms. (C) 2013 Published by Elsevier Ltd.

Original languageEnglish
Pages (from-to)427-440
Number of pages14
JournalPattern Recognition
Volume47
Issue number1
DOIs
Publication statusPublished - Jan 2014

Keywords

  • Clustering
  • Anisotropic
  • Gaussian Density
  • MEAN-SHIFT
  • IMAGE SEGMENTATION
  • ALGORITHM
  • RECOGNITION

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