By Barry de Ville
ISBN-10: 0585458421
ISBN-13: 9780585458427
ISBN-10: 1555582427
ISBN-13: 9781555582425
Microsoft facts Mining methods info mining from the actual point of view of IT execs utilizing Microsoft information administration applied sciences. the writer explains the hot information mining features in Microsoft's SQL Server 2000 database, trade Server, and different items, information the Microsoft OLE DB for information Mining normal, and provides readers most sensible practices for utilizing them all. The booklet bridges the formerly really good box of knowledge mining with the recent applied sciences and techniques which are fast making it an immense mainstream software for corporations of all sizes. info mining refers to a suite of applied sciences and methods in which IT execs seek huge databases of data (such as these contained through SQL Server) for styles and developments. regularly very important in finance, telecommunication, and different information-intensive fields, information mining more and more is helping businesses larger comprehend and serve their buyers through revealing deciding to buy styles and similar pursuits. it really is turning into a beginning for e-commerce and information administration. exact booklet on a sizzling information administration topicPart of electronic Press's SQL Server and knowledge mining clustersAuthor is knowledgeable on either conventional and Microsoft information mining applied sciences
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Additional resources for Microsoft Data Mining: Integrated Business Intelligence for e-Commerce and Knowledge Management
Sample text
KDD is strongly rooted in the scientific tradition and incorporates state-ofthe-art knowledge developed through a series of KDD conferences and industry working groups that have been wrestling with the knowledge management issues in this area over the last decade. 2 The scientific method and the paradigms that come with it 25 approaches to describe the KDD process. org/). At this point Microsoft does not appear to have developed a KDD process, nor have they endorsed a given approach. com/data).
This means that I need customer observations through time and I need an indicator of defection. Once I do this, I will have an indicator of tenure, an interaction indicator, and a defection indicator on my data set. The test of my hypothesis is simple: All things considered, I expect that a complaint indicator for newer customers will be associated with more defections than would be the case with long-term customers. The business advice in this simple explanation is correspondingly simple: Pay more attention to particular kinds of customer complaints if the customer is a newcomer!
It also aims to reduce the attraction and subsequent loss of customers through low-cost, low-margin recruitment campaigns, which, over the life cycle of the affected customer, may cost more to manage than the income produced by the customer. Mellon Bank of Pittsburgh is using Intelligent Miner to analyze data on the bank’s existing credit card customers to characterize their behavior and predict, for example, which customers are most likely to take their business elsewhere. “We decided it was important for us to generate and manage our own attrition models,” said Peter Johnson, vice president of the Advanced Technology Group at Mellon Bank.
Microsoft Data Mining: Integrated Business Intelligence for e-Commerce and Knowledge Management by Barry de Ville
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