Design of Blurring Mean-Shift Algorithms for Data Classification

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...

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Bibliographic Details
Published in:Journal of classification 2016-07, Vol.33 (2), p.262-281
Main Author: Grillenzoni, Carlo
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Blurring
Centroids
Classification
Clustering
Computer simulation
Data analysis
Marketing
Mathematics and Statistics
Pattern Recognition
Psychometrics
Signal,Image and Speech Processing
Statistical Theory and Methods
Statistics
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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.
ISSN:0176-4268
1432-1343
DOI:10.1007/s00357-016-9205-7