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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

Abstract
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.