Unsupervised hierarchical clustering analysis

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). 

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. 

Hierarchical cluster analysis (HCA) is an unsupervised technique that examines the inteipoint distances between all of the samples and represents that information in the form of a twcKlimensional plot called a dendrogram. These dendrograms present the data from high-dimensional row spaces in a form that facilitates the use of human pattern-recognition abilities. 

Preliminary data analysis carried out for the spectral datasets were functional group mapping, and/or hierarchical cluster analysis (HCA). This latter method, which is well described in the literature,4,9 is an unsupervised approach that does not require any reference datasets. Like most of the multivariate methods, HCA is based on the correlation matrix Cut for all spectra in the dataset. This matrix, defined by Equation (9.1),... 

An in-depth review of statistical methods for metabonomic data analysis is beyond the scope of this chapter. Briefly, there are a few main approaches to data analysis. Examples of multivariate data analyses include the so-called unsupervised analyses such as PCA, independent component analysis (ICA), and hierarchical clustering analysis (HCA), while partial least square differential analysis (PLS-DA) is... 

These various chemometrics methods are used in those works, according to the aim of the studies. Generally speaking, the chemometrics methods can be divided into two types unsupervised and supervised methods(Mariey et al., 2001). The objective of unsupervised methods is to extrapolate the odor fingerprinting data without a prior knowledge about the bacteria studied. Principal component analysis (PCA) and Hierarchical cluster analysis (HCA) are major examples of unsupervised methods. Supervised methods, on the other hand, require prior knowledge of the sample identity. With a set of well-characterized samples, a model can be trained so that it can predict the identity of unknown samples. Discriminant analysis (DA) and artificial neural network (ANN) analysis are major examples of supervised methods. 

The principle of unsupervised learning consists in the partition of a data set into small groups to reflect, in advance, unknown groupings [YARMUZA, 1980] (see also Section 5.3). The results of the application of methods of hierarchical agglomerative cluster analysis (see also [HENRION et al., 1987]) were representative of the large palette of mathematical algorithms in cluster analysis. 

In this passage we demonstrate that comparable results may also be obtained when other methods of unsupervised learning, e.g. the non-hierarchical cluster algorithm CLUPOT [COOMANS and MASSART, 1981] or the procedure of the computation of the minimal spanning tree [LEBART et al., 1984], which is similar to the cluster analysis, are applied to the environmental data shown above. 

The second strategy of unsupervised learning is based on cluster analysis. With this method, the objects are aggregated stepwise according to the similarity of their features. As a result, hierarchically or nonhierarchically ordered clusters are formed. In order to describe the similarity of objects, we need to learn about appropriate similarity measures. 

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