Read e-book online A Practical Guide to Data Mining for Business and Industry PDF

By Shirley Coleman, Andrea Ahlemeyer-Stubbe

ISBN-10: 1118763726

ISBN-13: 9781118763728

Facts mining is easily on its strategy to turning into a well-known self-discipline within the overlapping parts of IT, information, computer studying, and AI. functional facts Mining for company offers a easy method of facts mining equipment, masking the common makes use of to which it's utilized. The method is complemented through case reports to create a flexible reference ebook, permitting readers to appear for particular tools in addition to for particular purposes. The publication is formatted to permit statisticians, desktop scientists, and economists to
cross-reference from a selected software or technique to sectors of curiosity.

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Additional resources for A Practical Guide to Data Mining for Business and Industry

Example text

6 Lift chart to compare models. 7 Fine scale lift chart. 9. Sometimes, the ability of the model to rank the customers in a relevant way is more important than the statistical quality of the models. A useful model is one that gives a credible rank ordering of customers in terms of associated variables. 8 Confusion matrix for comparing models. Another way to validate the models offered by most data mining software is cross-validation. This is a way of assessing how the results of the analysis will generalise to an independent dataset and includes a number of different ­methods.

4 Table of sample data. 40  Data mining Practicalities important, and so the average value is just the sum divided by 10. However, sometimes, the importance of data items may vary, and the weighted average value may be considered. For example, each of the expenditure values ­represents different segments of customers, and the segments have the following % weights: 10, 20, 5, 15, 20, 5, 5, 5, 5, 10. 7. The calculation is shown in the table. In our example the weighted average is higher than the average because the larger expenditure values generally occur in the more populated segments.

In many companies, there is hardly any distinction between the terms knowledge, information and data. Among other things in computer science and economics, it can be seen in the literature that there are strongly divergent views and that different approaches also exist within the two specialties. In computer science, the terms information and data are often used interchangeably, since an explicit distinction does not seem absolutely necessary. The data is equated with the information it represents.

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A Practical Guide to Data Mining for Business and Industry by Shirley Coleman, Andrea Ahlemeyer-Stubbe

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