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Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria

Fachada, N, ; Figueiredo, M. A. T. ; Lopes, V. ; Martins, R. C. ; Rosa, A. Rosa

Pattern Recognition Letters Vol. 45, Nº -, pp. 55 - 61, June, 2014.

ISSN (print): 0167-8655
ISSN (online):

Journal Impact Factor: 1,551 (in 2014)

Digital Object Identifier: 10.1016/j.patrec.2014.03.008

This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. The method is deterministic and produces dendrograms, which are important features for microbiologists. A set of experiments, performed on yeast spectrometric data and on synthetic data, show the new approach outperforms several well-known clustering algorithms, including techniques commonly used for microorganism differentiation.