Cluster analysis hierarchical clustering

Knowledge-gnided cluster analysis Hierarchical clustering Multidimensional clustering Axes scaling... 

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. 

When the peak of interest is strong and is not overlapped by other spectral features, data analyses are straightforward and individual wavelength-specific images can be constructed (18), In most cases, the sample complexity and, therefore, that of the spectrum, requires a more rigorous approach to data analyses which is facilitated by multivariate analytical techniques, e.g., cluster analysis, hierarchical analysis and the like that yield high quality detailed images which can reveal anatomical features of interest (18, 19). 

Such definitive classification may be achieved with the aid of multivariate pattern recognition techniques such as hierarchical clustering, linear discriminant analysis (LDA) and artificial neural network analysis. Hierarchical clustering techniques compare sets of data (e.g. individually acquired spectra or spectra acquired by mapping of tissue) and group the data according to some measure of similarity. For mapping data, the application of cluster analysis... 

In-line monitoring of EVA extrusion was carried out using in-line fibre optic Raman spectroscopy. Vinyl acetate content in the random copolymer and melt indices of various grades of the EVA were determined and vinyl acetate content in the samples correlated using linear least squares andPLS analysis. Hierarchical Cluster analysis was employed as a pattern recognition technique to follow the natural clustering tendencies of the EVA samples. 10 refs. 

In a typical appHcation of hierarchical cluster analysis, measurements are made on the samples and used to calculate interpoint distances using an appropriate distance metric. The general distance, is given by... 

Dubois M, Plaisance H, Thome JP, et al. 1996. Hierarchical cluster analysis of environmental pollutants through P450 induction in cultured hepatic cells. Ecotoxicol Environ Saf 34 205-215. 

Willett P, Winterman V, Bawden DJ. Implementation of non hierarchical cluster-analysis methods in chemical information-systems-selection of compounds for biological testing and clustering of substructure search output. Chem Inf Comp Sci 1986 26 109-18. 

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. 

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. 

One of the most used techniques of non-hierarchical cluster analysis is the density method (potential method). The high density of objects in the m-dimension that characterizes clusters is estimated by means of a density function (potential function) P. For this, the objects are modelled by Gaus-... 

Figure 20.13 Unsupervised hierarchical cluster analysis of nine FFPE leiomyomas from 1990-2002 and one FFPE sarcoma from 1980. Reproduced with permission from Reference 22.

Unsupervised hierarchical cluster analysis showed clear separation between the sarcoma and the leiomyomas but did not reveal associations among the leiomyomas based on storage time, possibly indicating that individual differences exceeded any differences caused by storage (Fig. 20.13). 

Hierarchical cluster analysis (HCA) also provides a method of determining... 

---