The k-NN algorithm for compositional data: a revised approach with and without zero values present

Michail Tsagris

MPRA Paper from University Library of Munich, Germany

Abstract: In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science among others. The goal of this paper is to extend the taxicab metric and a newly suggested metric for com-positional data by employing a power transformation. Both metrics are to be used in the k-nearest neighbours algorithm regardless of the presence of zeros. Examples with real data are exhibited.

Keywords: compositional data; entropy; k-NN algorithm; metric; supervised classification (search for similar items in EconPapers)
JEL-codes: C18 (search for similar items in EconPapers)
Date: 2014-07
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Published in Journal of Data Science 12.3(2014): pp. 519-534

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