Classification hierarchical cluster analysis

I. Bondarenko, H. Van Malderen, B. Treiger, P. Van Espen and R. Van Grieken, Hierarchical cluster analysis with stopping rules built on Akaike s information criterion for aerosol particle classification based on electron probe X-ray microanalysis. Chemom. Intell. Lab. Syst., 22 (1994) 87-95. 

A number of chemometric tools have been employed for these classifications, including partial least squares - hierarchical cluster analysis (PLS-HCA) for Viagra tablets [98] and antimalarial artesunate tablets [99]. de Peinder et al. used partial least squares discriminant analysis (PLS-DA) models to distinguish genuine from counterfeit Lipitor tablets even when the real API was present [100]. The counterfeit samples also were found to have poorer API distribution than the genuine ones based on spectra collected in a cross pattern on the tablet. 

So, clustering techniques have been used for classification. Piepponen et al. applied a hierarchical cluster analysis (CLUSTAN) to the classification of food oils (groundnut, soya, sunflower and maize) by their fatty acid composition. The dendrogram of the distances shows four weU-separated clusters. Some suspect commercial samples of sunflower oil fall near the cluster of soya oils, so far from the clainud class that they cannot be consider i genuine. 

A detailed analysis of coding properties of ORNs in B sensilla of palps and antennae has revealed 22 functional types of sensilla, most with two ORNs and one with four (de Bruyne et al, 1999,2001). Hierarchical cluster analysis indicates that populations of ORNs can be categorized in distinct classes, with differences between classes larger than within (de Bruyne et al, 1999). The physiological classification of this large set of neurons, representing more than half of all... 

One of the emerging biological and biomedical application areas for vibrational spectroscopy and chemometrics is the characterization and discrimination of different types of microorganisms [74]. A recent review of various FTIR (Fourier transform infrared spectrometry) techniques describes such chemometrics methods as hierarchical cluster analysis (HCA), principal component analysis (PCA), and artificial neural networks (ANN) for use in taxonomical classification, discrimination according to susceptibility to antibiotic agents, etc. [74],... 

Hierarchical cluster analysis (HCA) and the closely related tree cluster analysis (TCA) provide a simple view of distances between samples, often viewed in a tree-like structure called a dendrogram (see Fig. 6a as an example). These types of analyses methods allow for the development of quick and simple classification schemes. Distances are calculated between all samples within the data set where the data parameters are the coordinates in a multidimensional variable parameter space (of dimension Mvar)- The general distance... 

Fig. 23 Classification of the NSAID dataset based on three-dimensional autocorrelation descriptors, a) Hierarchical clustering analysis (HCA). The dark gray cluster includes the COX-2-selective drugs, b) Visualization of the minimal spanning tree (MST). The longest connections are drawn as dotted lines in order to derive classes of compounds.

Suzuki, T, Ide, K., Ishida, M. and Shapiro, S. (2001) Classification of environmental estrogens by physico-chemical properties using principal component analysis and hierarchical cluster analysis. /. Chem. Inf. Comput. Sci., 41, 718-726. 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

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