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A fresh look at mean-shift based modal clustering

Ameijeiras-Alonso, Jose; Einbeck, Jochen


Jose Ameijeiras-Alonso


Modal clustering is an unsupervised learning technique where cluster centers are identified as the local maxima of nonparametric probability density estimates. A natural algorithmic engine for the computation of these maxima is the mean shift procedure, which is essentially an iteratively computed chain of local means. We revisit this technique, focusing on its link to kernel density gradient estimation, in this course proposing a novel concept for bandwidth selection based on the concept of a critical bandwidth. Furthermore, in the one-dimensional case, an inverse version of the mean shift is developed to provide a novel approach for the estimation of antimodes, which is then used to identify cluster boundaries. A simulation study is provided which assesses, in the univariate case, the classification accuracy of the mean-shift based clustering approach. Three (univariate and multivariate) examples from the fields of philately, engineering, and imaging, illustrate how modal clusterings identified through mean shift based methods relate directly and naturally to physical properties of the data-generating system. Solutions are proposed to deal computationally efficiently with large data sets.


Ameijeiras-Alonso, J., & Einbeck, J. (2023). A fresh look at mean-shift based modal clustering. Advances in Data Analysis and Classification,

Journal Article Type Article
Acceptance Date Nov 11, 2023
Online Publication Date Dec 14, 2023
Publication Date Dec 14, 2023
Deposit Date Jan 4, 2024
Publicly Available Date Dec 15, 2024
Journal Advances in Data Analysis and Classification
Print ISSN 1862-5347
Electronic ISSN 1862-5355
Publisher Springer
Peer Reviewed Peer Reviewed
Keywords Applied Mathematics; Computer Science Applications; Statistics and Probability
Public URL


This file is under embargo until Dec 15, 2024 due to copyright restrictions.

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