By David L. Olson Dr., Dursun Delen Dr. (auth.)
This e-book covers the elemental options of knowledge mining, to illustrate the opportunity of collecting huge units of knowledge, and reading those info units to achieve invaluable enterprise knowing. The ebook is geared up in 3 components. half I introduces strategies. half II describes and demonstrates uncomplicated info mining algorithms. It additionally includes chapters on a couple of assorted innovations frequently utilized in facts mining. half III focusses on enterprise functions of information mining. equipment are offered with basic examples, purposes are reviewed, and relativ merits are evaluated.
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This highlights another limitation of the method, in that all five measures are treated equally, even though State probably has nothing to do with outcome, while experience may be critical. 4. This result was terrible. It did not correctly classify any cases. The actually excellent applicant was classified as unacceptable. 4. 5. Matching past job applicant data – New case Record 1 2 3 4 5 6 7 8 9 10 Age 25–30 >30 25–30 <25 25–30 25–30 25–30 25–30 25–30 <25 State CA NV CA CA CA CA CA OR CA CA Degree BS Masters Masters BS BS Masters BS Masters BS BS Major Eng Bus Ad Comp Sci IS IS Bus Ad Eng Comp Sci IS IS Experience 1–2 years >2 years 0 0 1–2 years 0 >2 years 1–2 years 1–2 years 1–2 years Outcome Excellent Adequate Adequate Unacceptable Minimal Excellent Adequate Adequate Minimal Adequate Match 0 3 1 0 0 1 1 1 0 0 unacceptable candidate was classified as adequate.
Li (2006). Association rule-generation algorithm for mining automotive warranty data, International Journal of Production Research 44:14, 2749–2770. R. Agrawal, T. Izmielinski, A. Swami (1993). Database mining: A performance perspective, IEEE Transactions on Knowledge and Data Engineering 5:6, 914–925; J. Han, J. Pei, Y. Yin (2000). Mining frequent patterns without candidate generation, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, United States, 1–12.
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. The 17 variables identified in the discriminant analysis were used for the other two models. The same training and test sets were employed. The training set was used to build a rule-based classifier model.