Critical Scale for Unsupervised Cluster Discovery

Authors:
Tomoya Sakai;Atsushi Imiya;Takuto Komazaki;Shiomu Hama
Affiliations:
Institute of Media and Information Technology, Chiba University, Japan;Institute of Media and Information Technology, Chiba University, Japan;Graduate School of Science and Technology, Chiba University, Japan;Graduate School of Science and Technology, Chiba University, Japan
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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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.