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Design of Blurring Mean-Shift Algorithms for Data Classification

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Abstract

The mean-shift algorithm is an iterative method of mode seeking and data clustering based on the kernel density estimator. The blurring mean-shift is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. It converges to local centroids, but may suffer from problems of asymptotic bias, which fundamentally depend on the design of its smoothing components. This paper develops nearest-neighbor implementations and data-driven techniques of bandwidth selection, which enhance the clustering performance of the blurring method. These solutions can be applied to the whole class of mean-shift algorithms, including the iterative local mean method. Extended simulation experiments and applications to well known data-sets show the goodness of the blurring estimator with respect to other algorithms.

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Authors and Affiliations

  1. IUAV University, Venice, Italy

    Carlo Grillenzoni

  2. Institute of Architecture, University of Venice, S. Croce 1957, 30135, Venezia, Italy

    Carlo Grillenzoni

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  1. Carlo Grillenzoni
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Correspondence to Carlo Grillenzoni.

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Grillenzoni, C. Design of Blurring Mean-Shift Algorithms for Data Classification. J Classif 33, 262–281 (2016). https://doi.org/10.1007/s00357-016-9205-7

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  • DOI: https://doi.org/10.1007/s00357-016-9205-7

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