By David L. Olson Dr., Dursun Delen Dr. (auth.)

This publication covers the basic thoughts of knowledge mining, to illustrate the potential for accumulating huge units of information, and studying those facts units to achieve beneficial enterprise realizing. The e-book is geared up in 3 components. half I introduces options. half II describes and demonstrates uncomplicated facts mining algorithms. It additionally comprises chapters on a couple of various concepts frequently utilized in info mining. half III focusses on enterprise purposes of knowledge mining. equipment are provided with basic examples, purposes are reviewed, and relativ merits are evaluated.

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Kandel, H. Bunke (2001). Geometric approach to data mining, International Journal of Image & Graphics 2, Apr, 363–386. 1. 1. A simple application of Hamming distance is to categorize new applicants by the number of categories that match. Hamming distance is often measured as the number of negative cases (mismatches rather than matches). This has the feature that a smaller number indicates a closer relationship. Here we use the more direct measure of the number of matches. The results will be identical.

8. Coincidence matrix – combined models Telephone bill Actual Insolvent Actual Solvent Model insolvent 19 1 20 Model solvent 17 626 643 Unclassified 28 27 91 Total 64 654 718 Stage 6. Deployment Every customer was examined using all three algorithms. If all three agreed on classification, that result was adopted. If there was disagreement in the model results, the customer was categorized as unclassified. 8. 8% of the cases. But only one actually solvent customer would have been disconnected without further analysis.

A coincidence matrix shows the count of cases correctly classified, as well as the count of cases classified in each incorrect category. But in many data mining studies, the model may be very good at classifying one category, while very poor at classifying another category. The primary value of the coincidence matrix is that it identifies what kinds of errors are made. It may be much more important to avoid one kind of error than another. For instance, a bank loan officer suffers a great deal more from giving a loan to someone who’s expected to repay and does not than making the mistake of not giving a loan to an applicant who actually would have paid.

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