Implementing Data Mining Algorithms In Microsoft Sql Server

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Implementing Data Mining Algorithms In Microsoft Sql Server

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Author : Claudio Luiz Curotto
language : en
Publisher: WIT Press
Release Date : 2005

Implementing Data Mining Algorithms In Microsoft Sql Server written by Claudio Luiz Curotto and has been published by WIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.

"All source codes, as well as data sets used in computational experiments, are included in the accompanying CD-ROM." -- p. [xv]

Microsoft Data Mining

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Author : Barry de Ville
language : en
Publisher: Elsevier
Release Date : 2001-05-17

Microsoft Data Mining written by Barry de Ville and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05-17 with Computers categories.

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

Data Mining With Microsoft Sql Server 2008

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Author : Jamie MacLennan
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-10

Data Mining With Microsoft Sql Server 2008 written by Jamie MacLennan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-10 with Computers categories.

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.

Data Mining With Sql Server 2005

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Author : ZhaoHui Tang
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-03

Data Mining With Sql Server 2005 written by ZhaoHui Tang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-03 with Computers categories.

Your in-depth guide to using the new Microsoft data mining standard to solve today's business problems Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects. You'll learn: The principal concepts of data mining How to work with the data mining algorithms included in SQL Server data mining How to use DMX-the data mining query language The XML for Analysis API The architecture of the SQL Server 2005 data mining component How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms How to implement a data mining project using SQL Server Integration Services How to mine an OLAP cube How to build an online retail site with cross-selling features How to access SQL Server 2005 data mining features programmatically

Exam 70 463 Implementing A Data Warehouse With Microsoft Sql Server 2012 Training Kit

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Author : Dejan Sarka, Matija Lah, and Grega Jerkic
language : en
Publisher: Microsoft Press
Release Date :

Exam 70 463 Implementing A Data Warehouse With Microsoft Sql Server 2012 Training Kit written by Dejan Sarka, Matija Lah, and Grega Jerkic and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Data Mining For Business Analytics

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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14

Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Mathematics categories.

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

Beginning Microsoft Sql Server 2008 Programming

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Author : Robert Vieira
language : en
Publisher: John Wiley & Sons
Release Date : 2011-01-31

Beginning Microsoft Sql Server 2008 Programming written by Robert Vieira and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-31 with Computers categories.

This comprehensive introduction to SQL Server begins with an overview of database design basics and the SQL query language along with an in-depth look at SQL Server itself Progresses on to a clear explanation of how to implement fundamental concepts with the new 2008 version of SQL Server Discusses creating and changing tables, managing keys, writing scripts, working with stored procedures, programming with XML, using SQL Server Reporting and Integration Services, and more Features updated and new material, including new examples using Microsoft's AdventureWorks sample database

Smart Business Intelligence Solutions With Microsoft Sql Server 2008

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Author : Lynn Langit
language : en
Publisher: Microsoft Press
Release Date : 2009-02-04

Smart Business Intelligence Solutions With Microsoft Sql Server 2008 written by Lynn Langit and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-04 with Computers categories.

Get the end-to-end instruction you need to design, develop, and deploy more effective data integration, reporting, and analysis solutions using SQL Server 2008—whether you’re new to business intelligence (BI) programming or a seasoned pro. With real-world examples and insights from an expert team, you’ll master the concepts, tools, and techniques for building solutions that deliver intelligence—and business value—exactly where users want it. Discover how to: Manage the development life cycle and build a BI team Dig into SQL Server Analysis Services, Integration Services, and Reporting Services Navigate the Business Intelligence Development Studio (BIDS) Write queries that rank, sort, and drill down on sales data Develop extract, transform, and load (ETL) solutions Add a source code control system Help secure packages for deployment via encryption and credentials Use MDX and DMX Query Designers to build reports based on OLAP cubes and data mining models Create and implement custom objects using .NET code View reports in Microsoft Office Excel and Office SharePoint Serverook

Effective Crm Using Predictive Analytics

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Author : Antonios Chorianopoulos
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-19

Effective Crm Using Predictive Analytics written by Antonios Chorianopoulos and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-19 with Mathematics categories.

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

Encyclopedia Of Data Warehousing And Mining

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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2005-06-30

Encyclopedia Of Data Warehousing And Mining written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-30 with Computers categories.

Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.