Abstract
Mode-based clustering methods define clusters in terms of the modes of a density estimate. The most common mode-based method is mean shift clustering which defines clusters to be the basins of attraction of the modes. Specifically, the gradient of the density defines a flow which is estimated using a gradient ascent algorithm. Rodriguez and Laio (2014) introduced a new method that is faster and simpler than mean shift clustering. Furthermore, they define a clustering diagram that provides a simple, two-dimensional summary of the clustering information. We study the statistical properties of this diagram and we propose some improvements and extensions. In particular, we show a connection between the diagram and robust linear regression.
Citation
Isabella Verdinelli. Larry Wasserman. "Analysis of a mode clustering diagram." Electron. J. Statist. 12 (2) 4288 - 4312, 2018. https://doi.org/10.1214/18-EJS1510