A review of mean-shift algorithms for clustering

Abstract

A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the blurring and non-blurring versions of mean-shift; theoretical results about mean-shift algorithms and Gaussian mixtures; relations with scale-space theory, spectral clustering and other algorithms; extensions to tracking, to manifold and graph data, and to manifold denoising; K-modes and Laplacian K-modes algorithms; acceleration strategies for large datasets; and applications to image segmentation, manifold denoising and multivalued regression.

arXiv comment28 pages, 9 figures. Invited book chapter to appear in the CRC Handbook of Cluster Analysis (eds. Roberto Rocci, Fionn Murtagh, Marina Meila and Christian Hennig)

References in corpus (2)

• The K-modes algorithm for clustering
• Reconstruction of sequential data with density models

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