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