Exploratory data analysis clustering techniques

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Spectral features and their corresponding molecular descriptors are then applied to mathematical techniques of multivariate data analysis, such as principal component analysis (PCA) for exploratory data analysis or multivariate classification for the development of spectral classifiers [84-87]. Principal component analysis results in a scatter plot that exhibits spectra-structure relationships by clustering similarities in spectral and/or structural features [88, 89]. 

Cluster analysis will be discussed in Chapter 6 in detail. Here we introduce cluster analysis as an alternative nonlinear mapping technique for exploratory data analysis. The method allows gaining more insight into the relations between the objects if a... 

Exploratory data analysis is a collection of techniques that search for structure in a data set before calculating any statistic model [Krzanowski, 1988]. Its purpose is to obtain information about the data distribution, about the presence of outliers and clusters, and to disclose relationships and correlations between objects and/or variables. Principal component analysis and cluster analysis are the most well-known techniques for data exploration [Jolliffe, 1986 Jackson, 1991 Basilevsky, 1994]. 

Data sets can be analyzed by exploratory data analysis, usually based on multivariate techniques, such as principal component analysis cluster analysis allows the evaluation of... 

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. 

Cluster analysis is justifiably a popular and common technique for exploratory data analysis. Most commercial multivariate statistical software packages offer several algorithms, along with a wide range of graphical display facilities to aid the user in identifying patterns in data. Having indicated that... 

Cluster analysis is an exploratory data analysis technique aimed at grouping items (e.g. chemicals and their properties) into clusters of similar items according to their position in the multidimensional parameter space. The various clustering methods differ mainly in how they calculate the distance from a point to the cluster it may be to the nearest point, the most distant point or the centroid of the cluster, with the result that the respective clusters will have different shapes. 

There is no answer to the question about which of these techniques performs best. Clustering is an exploratory data analysis procedure the choice of which technique to be used for clustering often comes from a very good understanding of the objects to be clustered. A tutorial on clustering methods used in computational chemistry has appeared in this series and should be consulted. ... 

Exploratory modeling using modem statistical modeling techniques such as generalized additive modeling (GAM) (15), cluster analysis, and tree-based modehng (TBM) to reveal structure in the data and initially select explanatory covariates. 

In the first mentioned type of application, electrophoretic data are subjected to exploratory analysis techniques, such as principal component analysis (PCA) (5-8), robust PCA (rPCA) (9-13), projection pursuit (PP) (6,14-18), or cluster analysis (8, 19, 20). They all result in a simple low-dimensional visualization of the multivariate data. As a consequence, it will be easier for the analyst to get insight in the data in order to see whether there is a given... 

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