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Management Information SystemsInternational EditionData MiningNEWINTRODUCTION TO BUSINESS DATA MININGby David L Olson, Uni ver si ty of Nebraska - Lincoln, and Yong Shi,University of Nebraska-Omaha2007 (November 2005) / 336 pagesISBN-13: 978-0-07-295971-0 / MHID: 0-07-295971-1ISBN-13: 978-0-07-124470-1 / MHID: 0-07-124470-0 [IE]Website: http://www.mhhe.com/olson1eIntroduction to Business Data Mining was developed tointroduce students, as opposed to professional practitionersor engineering students, to the fundamental concepts of datamining. Most importantly, this text shows readers how togather and analyze large sets of data to gain useful businessunderstanding. A four part organization introduces thematerial (Part I), describes and demonstrated basic data miningalgorithms (Part II), focuses on the business applications of datamining (Part III), and presents an overview of the developingareas in this field, including web mining, text mining, and theethical aspects of data mining. (Part IV). The author team has hadextensive experience with the quantitative analysis of businessas well as with data mining analysis. They have both taught thismaterial and used their own graduate students to prepare thetext’s data mining reports. Using real-world vignettes and theirextensive knowledge of this new subject, David Olson andYong Shi have created a text that demonstrates data miningprocesses and techniques needed for business applications.FEATURES• Coverage of business applications: This text focuses on the valueof data analyses to business decision making while also exploringconcepts such as lift, customer relationship management, marketsegmentation, and more.• Straightforward explanation of methods, demonstrated withexamples: Short vignettes are used throughout showing how specificconcepts have been applied in actual business situations. Referencesto data mining software and websites are also featured.• Major software addressed: The text’s appendices show how majorsoftware projects support various aspects of data mining. Also, thetext reviews popular data mining software to help students becomefamiliar with the software options available in data mining.• Concepts of data mining introduced early: Concept overviewsprecede the discussion of data mining algorithms, allowing readersto understand the importance of techniques by seeing how they areapplied before they actually learn them.CONTENTSPart I: INTRODUCTION. Chapter 1: Initial Description of Data Mining inBusiness. Chapter 2: Data Mining Processes and Knowledge Discovery. Chapter3: Database Support to Data Mining. Part II: DATA MINING METHODS ASTOOLS. Chapter 4: Overview of Data Mining Techniques. Chapter 4 Appendix:Enterprise Miner Demonstration on Expenditure Data Set. Chapter 5: ClusterAnalysis. Chapter 5 Appendix: Clementine. Chapter 6: Regression Algorithms inData Mining. Chapter 7: Neural Networks in Data Mining. Chapter 8: DecisionTree Algorithms. Appendix 8: Demonstration of See5 Decision Tree Analysis.Chapter 9: Linear Programming-Based Methods. Chapter 9 Appendix: Data MiningLinear Programming Formulations. Part III: BUSINESS APPLICATIONS. Chapter10: Business Data Mining Applications Applications. Chapter 11: Market-BasketAnalysis. Chapter 11 Appendix: Market-Basket Procedure. Part IV: DEVELOPINGISSUES. Chapter 12: Text and Web Mining. Chapter 12 Appendix: Semantic TextAnalysis. Chapter 13: Ethical Aspects of Data MiningDatabase Management(Professional References)SQL SERVER 2005: THE COMPLETE REFERENCESecond Editionby Jeffrey Shapiro and Steen Bowman2006 (March 2006)ISBN-13: 978-0-07-226152-3 / MHID: 0-07-226152-8An Osborne Media TitleProfessional Book• Enterprise data management capabilities, including securityand clustering• Powerful developer tools -- T-SQL, .NET, CLR, XML, ADO.NET 2.0• Business Intelligence features, such as Integration Services,data warehousing, and reportsINVITATION TO PUBLISHMcGraw-Hill is interestedin reviewing manuscriptfor publication. Pleasecontact your localMcGraw-Hill office or email toasiapub@mcgraw-hill.comVisit McGraw-Hill Education (Asia)Website: www.mcgraw-hill.com.sg10993-110_MIS.indd 10911/15/06 5:27:23 PM