Pattern Recognition Methods for Objectively Classifying Bacteria

5 PATTERN RECOGNITION METHODS FOR OBJECTIVELY CLASSIFYING BACTERIA 

When describing mathematical modeling in general (not just for classification of bacteria), it is important to point out the mathematical meaning of pattern recognition the mapping of an n-dimensional function to describe a set of 

Two examples of unsupervised classical pattern recognition methods are hierarchical cluster analysis (HCA) and principal components analysis (PCA). Unsupervised methods attempt to discover natural clusters within data sets. Both HCA and PCA cluster data. 

Classical supervised pattern recognition methods include /( -nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA). Both 

In contrast, SIMCA uses principal components analysis to model object classes in the reduced number of dimensions. It calculates multidimensional boxes of varying size and shape to represent the class categories. Unknown samples are classified according to their Euclidean space proximity to the nearest multidimensional box. Kansiz et al. used both KNN and SIMCA for classification of cyanobacteria based on Fourier transform infrared spectroscopy (FTIR).44 

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