Advanced Data Mining Techniques by David L. Olson Dr., Dursun Delen Dr. (auth.)

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

This booklet covers the elemental thoughts of information mining, to illustrate the possibility of accumulating huge units of knowledge, and examining those facts units to achieve helpful company realizing. The ebook is equipped in 3 elements. half I introduces thoughts. half II describes and demonstrates easy facts mining algorithms. It additionally comprises chapters on a couple of various recommendations usually utilized in information mining. half III focusses on company functions of information mining. equipment are provided with basic examples, purposes are reviewed, and relativ benefits are evaluated.

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This is a relatively simple idea, allowing the user to assign relative costs by type of error. For instance, if our model predicted that an account was insolvent, that might involve an average write-off of $200. On the other hand, waiting for an account that ultimately was repaid might involve a cost of $10. Thus, there would be a major difference in the cost of errors in this case. Treating a case that turned out to be repaid as a dead account would risk the loss of $190, in addition to alienating the customer (which may or may not have future profitability implications).

A database perspective on knowledge discovery, Communications of the ACM 39:11, 58–64. 17 SQL was used by those researchers to generate sets of attributes that were useful in identifying item clusters. Lee and Liu suggested a way to utilize agent technology to expedite Web mining that utilized SQL. Grossman et al. 18 Key measures in association rule mining include support and confidence. Support refers to the degree to which a relationship appears in the data. Confidence relates to the probability that if a precedent occurs, a consequence will occur.

Independent test data provides a much better test. The accuracy for insolvent customers, which is very important because it costs so much more, decreased from 78% in the training data to 56% in the test data. 4. 5. Coincidence matrix – unequal misclassification costs Telephone bill Model insolvent Model solvent Actual Insolvent 36 28 Actual Solvent 22 632 58 660 64 654 718 Handling Data 33 From a total cost perspective, the model utilizing unequal misclassification costs (using real costs) was considered more useful.

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