Data exploration techniques

Mudlogging is another important direct data gathering technique, which was discussed in some detail in Section 2.2, Exploration Methods and Techniques. 

Another major change was the shift from extensive use of field laboratory exploration techniques to the laboratory techniques hke ICP-AES and INAA. These produce a higher quality data than had resulted from the dc arc and other field techniques, with respect to both repeatability of measurement and improved detection limits. The metrology laboratory certifications for As and Hg in soils and sediments as key environmental toxins provided strong support to mineral exploration programs. 

Oliveri et al. (2009) presented the development of an artificial tongue based on cyclic voltammetry at Pt microdisk electrodes for the classification of olive oils according to their geographical origin the measurements are made directly in the oil samples, previously mixed with a proper quantity of a RTIL (room temperature ionic liquid). The pattern recognition techniques applied were PCA for data exploration and fc-NN for classification, validating the results by means of a cross-validation procedure with five cancellation groups. 

A data matrix produced by compositional analysis commonly contains 10 or more metric variables (elemental concentrations) determined for an even greater number of observations. The bridge between this multidimensional data matrix and the desired archaeological interpretation is multivariate analysis. The purposes of multivariate analysis are data exploration, hypothesis generation, hypothesis testing, and data reduction. Application of multivariate techniques to data for these purposes entails an assumption that some form of structure exists within the data matrix. The notion of structure is therefore fundamental to compositional investigations. 

Fig. 3 Fractal dimensions can be used to evaluate the rugged structure of fine particles. (A) Fractal dimensions used to describe the ruggedness of various lines (B) physical basis of the equipaced exploration technique for evaluating the fractal dimensions of rugged boundaries (C) data generated by the equipaced exploration technique for the profile of (B) c5s, structural boundary fractal dimension c5x, textural boundary fractal dimension.

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]. 

Chemometric techniques can be valuable tools for the exploration of CE data as well as for the classihcation of samples based on electrophoretic data. The techniques maximally exploit the multivariate character of the data. In several applications, it was demonstrated that chemometric approaches can extract more information from electropherograms than only a visual inspection can. It is very important, especially when using entire electropherograms, that the CE data are preprocessed (e.g., aligned) in an appropriate way prior to other chemometric calculations, because CE analyses generally exhibit a rather poor reproducibility. 

As we explore more complex scenes, more sophisticated data-processing techniques must be considered. The current forward modelling is limited to the knowledge of the single slit spectral information. These requirements include observations of known point-like sources to calibrate the instmment or taking advantage of techniques such as self-calibration, where a known point source in a complex scene is used to calibrate the system. 

Aguiar-Pulido V, Seoane JA, Gestal M, Dorado J (2013) Exploring patterns of epigenetic information with data mining techniques. Curr Pharm Des 19(4) 77W789... 

Qualitative data collection techniques are often a useful complement to surveys and at times are the only method that can be used to provide answers to key questions about a program s operations and performance. The most widely used techniques are key informant interviews, direct observation, and focus group (or community group) discussions. Qualitative information is collected through intensive, often repeated, interviews with individuals that are typically more in-depth than precoded questionnaires and explore why and how things are done or why people think something or respond or behave in certain ways because of the program. 

Generally, two types of questions are asked when applying multivariate data analysis techniques one question aims to explore the gathered data without ary preconceived assumptions or notions, while the second question relates to sample classification and finding valid and powerful models for prediction purposes. 

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