Critical Scale for Unsupervised Cluster Discovery

  • Tomoya Sakai
  • Atsushi Imiya
  • Takuto Komazaki
  • Shiomu Hama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)


This paper addresses the scale-space clustering and a validation scheme. The scale-space clustering is an unsupervised method for grouping spatial data points based on the estimation of probability density function (PDF) using a Gaussian kernel with a variable scale parameter. It has been suggested that the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical properties of the lifetime, however, are unclear. In this paper, we propose a concept of the ‘critical scale’ and explore perspectives on handling it for the cluster validation.


Probability Density Function Scale Space Critical Curve Valid Cluster Lifetime Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tomoya Sakai
    • 1
  • Atsushi Imiya
    • 1
  • Takuto Komazaki
    • 2
  • Shiomu Hama
    • 2
  1. 1.Institute of Media and Information Technology, Chiba UniversityJapan
  2. 2.Graduate School of Science and Technology, Chiba UniversityJapan

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