Cluster analysis, analytical methods

Multivariate chemometric techniques have subsequently broadened the arsenal of tools that can be applied in QSAR. These include, among others. Multivariate ANOVA [9], Simplex optimization (Section 26.2.2), cluster analysis (Chapter 30) and various factor analytic methods such as principal components analysis (Chapter 31), discriminant analysis (Section 33.2.2) and canonical correlation analysis (Section 35.3). An advantage of multivariate methods is that they can be applied in... 

A comprehensive two-volume Handbook of Chemometrics and Qualimetrics has been published by D. L. Massart et al. (1997) and B. G. M. Vandeginste et al. (1998) predecessors of this work and historically interesting are Chemometrics A Textbook (Massart et al. 1988), Evaluation and Optimization of Laboratory Methods and Analytical Procedures (Massart et al. 1978), and The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis (Massart and Kaufmann 1983). A classical reference is still Multivariate Calibration (Martens and Naes 1989). A dictionary with extensive explanations containing about 1700 entries is The Data Analysis Handbook (Frank and Todeschini 1994). 

In most applications chemometric methods are applied to analytical data in an off-line mode that is, data has already been obtained by conventional techniques and is then applied to a particular chemometric method. Examples of this use are in cluster analysis and in pattern recognition. They are applied to spectroscopic, chromatographic, and other analytical data. 

Cluster analysis Is used to determine the particle types that occur in an aerosol. These types are used to classify the particles in samples collected from various locations and sampling periods. The results of the sample classifications, together with meteorological data and bulk analytical data from methods such as instrunental neutron activation analysis (INAA). are used to study emission patterns and to screen samples for further study. The classification results are used in factor analysis to characterize spatial and temporal structure and to aid in source attribution. The classification results are also used in mass balance comparisons between ASEM and bulk chemical analyses. Such comparisons allow the combined use of the detailed characterizations of the individual-particle analyses and the trace-element capability of bulk analytical methods. 

Radioactive lateling of this cluster and neutron activation analysis of the g)ld enabled us to determine the extent of Nnding of the cluster to the particles. The results of both analytical methods show that a spacer of minimum length of about 10 A between the -SH group of a ribosomal protein and the N-atom on the cluster is n ed for significant binding. Preliminary experiments indicate that the producte of the derivatization reaction with SOS particles can be crystallized. 

Massart, D.L. and Kaufman, L. (1983) Hierarchical clustering methods. In The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis (ed. J.D. Winefordner), John Wiley and Sons, New York, pp. 75-99. 

To introduce grouping of analytical data based on unsupervised learning methods, that is, projection methods and cluster analysis... 

Broadly speaking, the statistical strategies of analysis can be classified into two families of methods, namely (i) factor analytical methods including, in particular, multidimensional scaling (MDS) and multiple correspondence analysis (MCA) and (ii) methods pertaining to cluster analysis and additive trees. As is usually the case, the choice of one method over another depends on several factors (i) the domain of application (i.e. traditionally, some methods are more popular than others in each particular domain of application) (ii) the individual preferences and background of each practitioner and (iii) the availability of appropriate (and user-friendly) software. 

Pre-qualification reduces a large set of initial suppliers to a smaller set of acceptable suppliers for further assessment. De Boer et al. (2001) have cited many different techniques for pre-qualification. Some of these techniques are categorical methods, data envelopment analysis (DEA), cluster analysis, case-based reasoning (CBR) systems, and multi-criteria decision making method (MCDM). Several authors have worked on pre-qualification of suppliers. Weber and Ellram (1992) and Weber et al. (2000) have developed DEA methods for pre-qualification. Hinkel et al. (1969) and Holt (1998) used cluster analysis for pre-qualification and finally Ng and Skitmore (1995) developed CBR systems for pre-qualification. Mendoza et al. (2008) developed a three phase multi-criteria method to solve a general supplier selection problem. The paper combines analytic hierarchy process (AHP) with goal programming for both pre-qualification and final order allocation. 

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