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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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. https://doi.org/10.1007/978-3-540-73499-4_17
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DOI: https://doi.org/10.1007/978-3-540-73499-4_17
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