Abstract
We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of our snipped k-means procedure. Simulations and a real data application to optical recognition of handwritten digits are used to illustrate and compare the approach.
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The author is grateful to an AE and two anonymous referees for very kind suggestions.
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Farcomeni, A. Snipping for robust k-means clustering under component-wise contamination. Stat Comput 24, 907–919 (2014). https://doi.org/10.1007/s11222-013-9410-8
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DOI: https://doi.org/10.1007/s11222-013-9410-8