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Applied Data Mining for Business and Industry by Paolo Giudici download in iPad, ePub, pdf

The data mining process is guided by the application. In this case it is necessary to specify a more appropriate method for the analysis. If any essential information is missing it will then be necessary to supply further data. However, this labelling does not make the variables into quantitative ones. For these it is also possible to establish connections and numerical relations among their levels.

Interpretation of the chosen

The data matrix is where data mining starts. In general, the creation of data marts to be analysed provides the fundamental input for the subsequent data analysis. In other situations, such as in the case of quantitative variables, the summary is done essentially with the aim of simplifying the analysis. The elements that are part of the creation of the database or databases and the subsequent analysis are closely interconnected.

This data will not necessarily

The inclusion of the data mining process in the company organisation must be done gradually, setting out realistic aims and looking at the results along the way. Data mining is not only data analysis, but also the integration of the results into the company decision process. We conclude this chapter with some useful references for the topics introduced in this chapter. We then consider multivariate distributions, starting with summary statistics for bivariate distributions and then moving on to multivariate exploratory analysis of qualitative data. For example, in the joint analysis of qualitative variables it is a good idea to transform the data matrix into a contingency table.

Finally, let us mention that some data are now observable in continuous rather than discrete time. It must be easy to use but also important enough to create interest. The choice of which method to use in the analysis depends on the problem being studied or on the type of data available.

This data will not necessarily be eliminated because it might contain information that is important in achieving the objectives of the analysis. Interpretation of the chosen model and its use in the decision process.