BecerraIM_ch13 - Instructor's Manual Knowledge Management:...

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Instructor's Manual Knowledge Management: Challenges, Solutions and Technologies 13-1 45 Chapter 13 Knowledge Discovery Systems: Systems That Create Knowledge Teaching Objectives To explain how knowledge is discovered To describe knowledge discovery systems, including design considerations, and how they rely on mechanisms and technologies To explain data mining (DM) technologies To discuss the role of DM in customer relationship management Key Terms The following alphabetical list identifies the key terms discussed in this chapter. The page number for each key term is provided. Aggregate sets, p. 270 Back propagation algorithm, p. 255 Combination, p. 249 CRISP-DM, p. 258 Customer profile exchange, (CPEX), p. 261 Data mining, p. 250 Data mining group (DMG), p. 261 Data preparation, p. 271 Data quality (DQ), p. 259 Data tombs, p. 268 Data warehouse, p. 259 Enterprise application integration (EAI), p. 266 Exploratory analysis of data (with OLAP), p. 260 Expertise-locator (KM) systems, p. 273 Human computer interface (HCI), p. 265 Inferential DM techniques, p. 254 Information retrieval (IR), p. 264 Knowledge discovery in databases (KDD), p. 254 Lateral thinking, p. 255 Linguistic analysis, p. 264 Market basket analysis, p. 254 Moore's law, p. 268 Paired-leaf analysis, p. 271 Personal profile, p. 257 Predictive DM techniques, p. 254 Rule induction algorithms, p. 260 Sample set, p. 260 Semantic analysis, p. 264
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Instructor's Manual Knowledge Management: Challenges, Solutions and Technologies 13-2 45 Similarity functions, p. 265 Socialization, p. 250 Storage law, p. 268 Target sell, p. 256 Ten-fold cross-validation, p. 261 Term frequency inverse document frequency (TFIDF), p. 264 Teaching Suggestions This Chapter presents the topic of knowledge discovery as: 1. Synthesis of new knowledge through socialization with other knowledgeable persons; or 2. Discovery by finding interesting patterns in observations, typically embodied in explicit data, through data mining technologies For graduate students, the instructor may wish to facilitate some creative brainstorming exercises focused on developing an innovative solution to a specific problem (like the one faced by the engineers at Westinghouse in Vignette 13.2). Also, for graduate students the section that focuses on DM technologies for data mining
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This note was uploaded on 04/06/2010 for the course MIS econ, mis, taught by Professor Mohammed during the Spring '10 term at École Normale Supérieure.

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BecerraIM_ch13 - Instructor's Manual Knowledge Management:...

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