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Design of blurring mean-shift algorithms for data classification. (English) Zbl 1349.62267
Summary: 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.
MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62G05 Nonparametric estimation
62G07 Density estimation
68T10 Pattern recognition, speech recognition
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