BecerraIM_ch12 - Instructor's Manual Discovering New...

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Discovering New Knowledge – Data Mining 12-1 45 Chapter 12 Discovering New Knowledge: Data Mining Teaching Objectives Introduce the student to the concept of knowledge discovery How it is different from knowledge elicitation from experts How it is different from extracting existing knowledge from databases The objectives of data mining Explanation of past events (descriptive DM) Prediction of future events (predictive DM) Introduce the student to the different classes of methods available for DM Symbolic (induction) Connectionist (neural networks) Statistical Introduce the student to the details of some of the methods described in the chapter Key Terms The following alphabetical list identifies the key terms discussed in this chapter. The page number for each key term is provided. Artificial neural networks (ANN), p. 228 Back propagation, p. 233 Classification, p. 217 Cluster analysis, p. 233 Curve fitting, p. 236 Discriminant analysis, p. 241 Induction tree, p. 220 Inductive learning, p. 220 K-means clustering, p. 240 Logistic regression, p. 241 Market basket analysis, p. 240 Multivariate correlation, p. 239 Nonlinear correlation, p. 239 Teaching Suggestions The important thing for undergraduates to capture is the mechanics of implementing an inductive algorithm such as C5.0. They should be familiar with what an inductive tree is and how the algorithm works. Graduate students should be able to understand the concepts of the C5.0 inductive algorithm and be able to do a simple example. Moreover, they should also understand the basics of neural networks and of the statistical techniques described. More importantly, however, they need to understand the concept of data mining and its importance to KM. The knowledge to be
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BecerraIM_ch12 - Instructor's Manual Discovering New...

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