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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Sakai, T., Imiya, A., Komazaki, T., & Hama, S. (2007). Critical scale for unsupervised cluster discovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4571 LNAI, pp. 218–232). Springer Verlag. https://doi.org/10.1007/978-3-540-73499-4_17
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