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- W4243944384 abstract "Free Access Bibliography Antonios Chorianopoulos, Antonios ChorianopoulosSearch for more papers by this author Book Author(s):Antonios Chorianopoulos, Antonios ChorianopoulosSearch for more papers by this author First published: 06 November 2015 https://doi.org/10.1002/9781119011583.biblio AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. References Anderson, Kristin. Customer relationship management. New York: McGraw-Hill, 2002. Berry, Michael J. A., and Gordon Linof. Mastering data mining: the art and science of customer relationship management. New York: John Wiley & Sons, Inc., 1999. Fernandez, George. Data mining using SAS applications. Boca Raton: Chapman & Hall/CRC, 2003. Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. 3rd ed. Morgan Kaufmann series in data management systems. Amsterdam: Elsevier/Morgan Kaufmann, 2012. Hughes, Arthur Middleton. Strategic database marketing. 4th ed. New York: McGraw-Hill, 2012. IBM. IBM SPSS Modeler 16 algorithms guide. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316. IBM. IBM SPSS Modeler 16 applications guide. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316. IBM. IBM SPSS Modeler 16 modeling nodes. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316. IBM Redbooks. Mining your own business in banking using DB2 Intelligent Miner for Data (IBM Redbooks). Armonk: IBM, 2001. http://www.redbooks.ibm.com/. IBM Redbooks. Mining your own business in retail using DB2 Intelligent Miner for Data. Armonk: IBM, 2001. http://www.redbooks.ibm.com/. IBM Redbooks. Mining your own business in telecoms using DB2 Intelligent Miner for Data (IBM Redbooks). Armonk: IBM, 2001. http://www.redbooks.ibm.com/. Larose, Daniel T. Discovering knowledge in data: an introduction to data mining. Hoboken: Wiley-Interscience, 2005. Linoff, Gordon S. Data analysis using SQL and excel. New York: John Wiley & Sons, Inc., 2007. Linoff, Gordon, and Michael J. A. Berry. Data mining techniques: for marketing, sales, and customer relationship management. 3rd ed. Indianapolis: Wiley, 2011. MacLennan, Jamie, Tang, Zhaohui, and Bogdan Crivat. Data mining with Microsoft SQL server 2008. Indianapolis: Wiley Publishing, Inc., 2009. Matignon, Randall. Data mining using SAS enterprise miner. Hoboken: John Wiley & Sons, Inc., 2007. Microsoft. SQL Server 2012 Data Mining. Washington, DC: Microsoft, 2012. https://msdn.microsoft.com/en-us/library/bb510516(v=sql.110).aspx. Nisbet, Robert, John F. Elder, and Gary Miner. Handbook of statistical analysis and data mining applications. Amsterdam: Academic Press/Elsevier, 2009. North, Matthew. Data mining for the masses. US: Global Text Project, 2012. Olson, David, and Dursun Delen. Advanced data mining techniques. Berlin: Springer-Verlag, 2008. Peelen, E. Customer relationship management. Upper Saddle River: Financial Times/Prentice Hall, 2005. RapidMiner. RapidMiner operator reference guide. RapidMiner, 2014. http://docs.rapidminer.com/. RapidMiner. RapidMiner studio manual. RapidMiner, 2014. http://docs.rapidminer.com/. Rud, Olivia Parr. Data mining cookbook: modeling data for marketing, risk and customer relationship management. New York: John Wiley & Sons, Inc., 2001. Shmueli, Galit, Nitin R. Patel, and Peter C. Bruce. Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner. 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2010. Tsiptsis, Konstantinos, and Antonios Chorianopoulos. Data mining techniques in CRM: inside customer segmentation. Chichester: John Wiley & Sons, Ltd, 2009. Witten, Ian H., Eibe Frank, and Mark A. Hall. Data mining: practical machine learning tools and techniques. 3rd ed. [Morgan Kaufmann series in data management systems]. Burlington: Morgan Kaufmann, 2011. Effective CRM Using Predictive Analytics ReferencesRelatedInformation" @default.
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