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A fast version of the k-means classification algorithm for astronomical applications

AuthorsOrdovás-Pascual, I. ; Sánchez Almeida, Jorge
Issue Date2014
PublisherEDP Sciences
CitationAstronomy and Astrophysics 565: A53 (2014)
Abstract[Context]: K-means is a clustering algorithm that has been used to classify large datasets in astronomical databases. It is an unsupervised method, able to cope very different types of problems. [Aims]: We check whether a variant of the algorithm called single pass k-means can be used as a fast alternative to the traditional k-means. [Methods]: The execution time of the two algorithms are compared when classifying subsets drawn from the SDSS-DR7 catalog of galaxy spectra. [Results]: Single-pass k-means turn out to be between 20% and 40% faster than k-means and provide statistically equivalent classifications. This conclusion can be scaled up to other larger databases because the execution time of both algorithms increases linearly with the number of objects. [Conclusions]: Single-pass k-means can be safely used as a fast alternative to k-means. © 2014 ESO.
Publisher version (URL)http://dx.doi.org/10.1051/0004-6361/201423806
Identifiersdoi: 10.1051/0004-6361/201423806
issn: 0004-6361
e-issn: 1432-0746
Appears in Collections:(IFCA) Artículos
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