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Author: Barry de Ville Publisher: Elsevier ISBN: 0080491847 Category : Computers Languages : en Pages : 338
Book Description
Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management. Unique book on a hot data management topic Part of Digital Press's SQL Server and data mining clusters Author is an expert on both traditional and Microsoft data mining technologies
Author: Barry de Ville Publisher: Elsevier ISBN: 0080491847 Category : Computers Languages : en Pages : 338
Book Description
Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management. Unique book on a hot data management topic Part of Digital Press's SQL Server and data mining clusters Author is an expert on both traditional and Microsoft data mining technologies
Author: Jamie MacLennan Publisher: John Wiley & Sons ISBN: 1118080009 Category : Computers Languages : en Pages : 14
Book Description
Eine praxisorientierte Einführung in das Data Mining Toolset des SQL Server 2008 und die neuen Data Mining Add-Ins für Office 2007. Enthält detaillierte Erläuterungen und Beispiele zu allen neuen Data Mining Features des SQL Server 2008. Gibt präzise Anleitungen zum Arbeiten mit den wichtigsten Data Mining-Algorithmen, (Naive Bayes-, Decision Trees-, Time Series-, Sequence Clustering-, Association- und Neural Network-Algorithmus), zum Data Mining in OLAP Datenbanken und mit SQL Server Integration Services 2008. Die begleitende Website enthält den kompletten Quellcode zu den Beispielen aus dem Buch.
Author: Barry de Ville Publisher: Digital Press ISBN: 9781555582425 Category : Computers Languages : en Pages : 344
Book Description
This guide teaches data mining from the perspective of IT professionals using Microsoft data management and e-commerce technologies. The book explains major new data mining capabilities in the forthcoming SQL Server 2000, Microsoft Commerce Server, and other products, and details the new Microsoft standard, "OLE DB for Data Mining".
Author: Galit Shmueli Publisher: John Wiley & Sons ISBN: 111954985X Category : Mathematics Languages : en Pages : 608
Book Description
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Author: Michael Alexander Publisher: John Wiley & Sons ISBN: 1118079183 Category : Computers Languages : en Pages : 558
Book Description
Chart a course for more effective data analysis with Access 2007. With this resource, you’ll learn how Access 2007 offers powerful functionality that may be better suited to your data analysis needs. Learn to analyze large amounts of data in meaningful ways, quickly and easily slice it into various views, automate redundant analysis, and save time—all using Access. If you know a bit about table structures and formulas as well as data analysis, start thinking outside the chart.
Author: Sivakumar Harinath Publisher: John Wiley & Sons ISBN: 1118059573 Category : Computers Languages : en Pages : 1058
Book Description
When used with the MDX query language, SQL Server Analysis Services allows developers to build full-scale database applications to support such business functions as budgeting, forecasting, and market analysis. Shows readers how to build data warehouses and multi-dimensional databases, query databases, and use Analysis Services and other components of SQL Server to provide end-to-end solutions Revised, updated, and enhanced, the book discusses new features such as improved integration with Office and Excel 2007; query performance enhancements; improvements to aggregation designer, dimension designer, cube and dimension wizards, and cell writeback; extensibility and personalization; data mining; and more
Author: Jamie MacLennan Publisher: John Wiley & Sons ISBN: 0470277742 Category : Computers Languages : en Pages : 14
Book Description
Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.
Author: Christopher M. Bishop Publisher: Springer ISBN: 9781493938438 Category : Computers Languages : en Pages : 0
Book Description
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author: Gordon S. Linoff Publisher: John Wiley & Sons ISBN: 0470952520 Category : Computers Languages : en Pages : 698
Book Description
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.
Author: Barry de Ville
Author: Barry de Ville
Author: Jamie MacLennan
Author: Barry de Ville
Author: Galit Shmueli
Author: Michael Alexander
Author: Sivakumar Harinath
Author: Jamie MacLennan
Author: Claudio Luiz Curotto
Author: Christopher M. Bishop
Author: Gordon S. Linoff