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Design of Blurring Mean-Shift Algorithms for Data Classification

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  • Carlo Grillenzoni

    (IUAV University
    University of Venice)

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.

Suggested Citation

  • Carlo Grillenzoni, 2016. "Design of Blurring Mean-Shift Algorithms for Data Classification," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 262-281, July.
    • Handle: RePEc:spr:jclass:v:33:y:2016:i:2:d:10.1007_s00357-016-9205-7
    DOI: 10.1007/s00357-016-9205-7
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    References listed on IDEAS

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    1. Ting-Li Chen, 2015. "On the convergence and consistency of the blurring mean-shift process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 157-176, February.
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    Cited by:

    1. Stefano Salata, 2023. "Filling the Gaps in Biophysical Knowledge of Urban Ecosystems: Flooding Mitigation and Stormwater Retention," Land, MDPI, vol. 12(3), pages 1-22, March.

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