Introduction to business data mining
著者
書誌事項
Introduction to business data mining
（The Irwin/McGraw-Hill series in operations and decision sciences）
McGraw-Hill/Irwin, c2007
大学図書館所蔵 件 / 全3件
注記
Includes index
内容説明・目次
内容説明
Introduction to Business Data Mining was developed to introduce students, as opposed to professional practitioners or engineering students, to the fundamental concepts of data mining. Most importantly, this text shows readers how to gather and analyze large sets of data to gain useful business understanding. A four part organization introduces the material (Part I), describes and demonstrated basic data mining algorithms (Part II), focuses on the business applications of data mining (Part III), and presents an overview of the developing areas in this field, including web mining, text mining, and the ethical aspects of data mining. (Part IV). The author team has had extensive experience with the quantitative analysis of business as well as with data mining analysis. They have both taught this material and used their own graduate students to prepare the text's data mining reports. Using real-world vignettes and their extensive knowledge of this new subject, David Olson and Yong Shi have created a text that demonstrates data mining processes and techniques needed for business applications.
目次
Part I: INTRODUCTIONChapter 1: Initial Description of Data Mining in BusinessChapter 2: Data Mining Processes and Knowledge DiscoveryChapter 3: Database Support to Data MiningPart II: DATA MINING METHODS AS TOOLSChapter 4: Overview of Data Mining TechniquesChapter 4 Appendix: Enterprise Miner Demonstration on Expenditure Data SetChapter 5: Cluster AnalysisChapter 5 Appendix: ClementineChapter 6: Regression Algorithms in Data MiningChapter 7: Neural Networks in Data MiningChapter 8: Decision Tree AlgorithmsAppendix 8: Demonstration of See5 Decision Tree AnalysisChapter 9: Linear Programming-Based MethodsChapter 9 Appendix: Data Mining Linear Programming FormulationsPart III: BUSINESS APPLICATIONSChapter 10: Business Data Mining ApplicationsApplicationsChapter 11: Market-Basket AnalysisChapter 11 Appendix: Market-Basket ProcedurePart IV: DEVELOPING ISSUESChapter 12: Text and Web MiningChapter 12 Appendix: Semantic Text AnalysisChapter 13: Ethical Aspects of Data Mining
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