Critical Scale for Unsupervised Cluster Discovery

  • Tomoya Sakai
  • Atsushi Imiya
  • Takuto Komazaki
  • Shiomu Hama
Conference paper

DOI: 10.1007/978-3-540-73499-4_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)
Cite this paper as:
Sakai T., Imiya A., Komazaki T., Hama S. (2007) Critical Scale for Unsupervised Cluster Discovery. In: Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science, vol 4571. Springer, Berlin, Heidelberg

Abstract

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.

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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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