Explorative data analysis

It is generally known that the repeatability of CE analyses is not optimal due to irreproducible flow rates (37). Therefore, it is recommended to align the corresponding peaks in the different electropherograms before chemometric data analysis (exploration or classification) is started. This alignment results in a data matrix, where the signals of the corresponding peaks of the... 

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

Evidence of the appHcation of computers and expert systems to instmmental data interpretation is found in the new discipline of chemometrics (qv) where the relationship between data and information sought is explored as a problem of mathematics and statistics (7—10). One of the most useful insights provided by chemometrics is the realization that a cluster of measurements of quantities only remotely related to the actual information sought can be used in combination to determine the information desired by inference. Thus, for example, a combination of viscosity, boiling point, and specific gravity data can be used to a characterize the chemical composition of a mixture of solvents (11). The complexity of such a procedure is accommodated by performing a multivariate data analysis. 

Consumption of natural gas, as of the mid-1990s, was about 2000 x 10 /yr. Using seismic detection equipment, exploration firms search for gas reserves buried deep underground and beneath the sea floor. Advanced computer systems process the seismic data to pinpoint the most likely locations for reserves. These advanced systems have both cut the time required for data analysis, by 80%, and gready improved the success rate for new drill rigs. 

Several statistical, quality management, and optimization data analysis tools, aimed at exploring records of measurements and uncover useful information from them, have been available for some time. However, all of them require from the user a signifieant number of assumptions and a priori decisions, which determine in a very strict manner the validity of the final results obtained. Furthermore, these classical tools are guided... 

It is felt that the use of electron microbeam methods offers the basis for a revolutionary new approach to the study of catalyst particles. Some results can be obtained immediately but to realise the full potential of the method a considerable amount of further exploration of data collection and data analysis methods will be needed. 

Sometimes the interpretation of analytical data does not need the deepest mathematical analysis but it is sufficient to get an impression on the structure of the data. Although the basic idea of graphical data interpretation is ancient (e.g., Brinton [1914]), the fundamentals of modern explorative data analysis (EDA) has been developed in the 1960s (Tukey [1962, 1977]). 

The most important methods of explorative data analysis concern the study of the distribution of the data and the recognition of outliers by boxplots (Fig. 8.18), histograms (Fig. 8.19), scatterplot matrices (Fig. 8.20), and various schematic plots. 

Garrett, R.G. 1983. Sampling Methodology. In Howarth, R.J. (ed.), Handbook of Exploration Geochemistry, Vol.2, Statistics and Data Analysis in Geochemical Prospecting. Elsevier, 83-110. 

D GIS provides a particularly useful method to address the challenges of deep mineral exploration as it permits spatial data analysis in a rigorous fashion (de Kemp 2007). Based on examples from the Noranda camp, one of Canada s most mature and economically important mining camps, the present paper highlights innovative approaches that can be... 

Especially in academic science, data analysis often starts as an exploratory creative process with evolving ideas of the data analysis flow and rapidly changing analysis parameters or conditions. Therefore, data analysis software has to be extremely flexible in order not to limit the exploration of data. Furthermore, it is important that the data analysis process is comprehensible and easily readable at all time points to ensure that scientists can share their approach with colleagues and to better prevent conceptual mistakes. A third requirement to data analysis software is the minimization of effort and time a scientist has to invest to implement various methods. 

The use of computers in science is widespread. Without powerful number-crunching facilities at his disposal, the modern scientist would be greatly handicapped, unable to perform the thousands or millions of calculations required to analyze his data or explore the implications of his favorite theory. He (or his assistant) thus requires at least some familiarity with computers, the programming of computers, and the methods which might be used by computers to solve his problems. An entire branch of mathematics, numerical analysis, exists to analyze the behavior of numerical algorithms. 

Graphical methods in connection with pattern recognition algorithms, i.e. geometrical or statistical methods, e.g. minimum spanning tree or cluster analysis, are more powerful methods for explorative data analysis than graphical methods alone. 

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

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