Clustering experimental design

Also, we do not cover several typical chemometrics types of analyses, such as cluster analysis, experimental design, pattern recognition, classification, neural networks, wavelet transforms, qualimetrics etc. This explains our decision not to include the word chemometrics in the title. 

Zemroch, P. J. (1986) Cluster analysis as an experimental design generator, with application to gasoline blending experiments. Technometrics 28, 39-49. 

Figure 5.119 shows that a number of standards lie very close to each other. This implies that the model will have a difficult time distinguishing between these samples. However, keep in mind that this plot shows only two of the six dimensions used in the model. The scores plot in Figure 5.120 shows the location of the samples in three dimensions (representing 99.98% of the spectral variance). The threcHiimensional view has been rotated to look down on the lines formed by varying temperature. Each cluster of points (noted by the number on the graph) contains all spectra collected on one standard. This view of the scores reproduces the experimental design (i.e., the standards are in the same position relative to each other in the scores plot as in the concentration plot, see Figure 5-42). This gives confidence that the measurements and the model accurately reflect the variation in the concentrations. Niuner-ous other scores plots can be examined for this rank six model, but they are not shown here.

Young, S. S., Ekins, S., and Lambert, C. G. (2002). So many targets, so many compounds, but so few resources. Current Drug Discovery, 1-6 (www.currentdrugdiscovery.com). Zemroch, P. J. (1986). Cluster analysis as an experimental design generator. Technometrics, 28, 39 19. 

The single evaluation set technique can be used reliably only if the splitting is performed by partitioning the objects by a well-stated criterion, such as a criterion based on experimental design or cluster analysis. 

Many different methods have been developed for compound selection. They include selection based on clustering compounds, dissimilarity-based selection, selection based on partitioning a collection of compounds into some multidimensional space, experimental design methods and the use of stochastic methods such as simulated annealing and genetic algorithms. Filtering techniques are often employed prior to compound selection to remove undesirable compounds. 

II. High-Dimensional Analysis Clustering analysis (Chapter 5), classification analysis (Chapter 6), multidimensional visualization (Chapter 7) in. Advanced Analysis Topics Statistical modeling (Chapter 8), experimental design (Chapter 9), statistical resampling methods (Chapter 10)... 

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