Data_Mining_with_Microsoft_SQL_Server_2008_(Wiley_2009)

时间:2016-11-09 04:54:39
【文件属性】:

文件名称:Data_Mining_with_Microsoft_SQL_Server_2008_(Wiley_2009)

文件大小:49.64MB

文件格式:ZIP

更新时间:2016-11-09 04:54:39

数据仓库 数据挖掘 SQLServer

Microsoft SQL Server 2008 is the third version of SQL Server that ships with included data mining technology. Since it was introduced in SQL Server 2000, data mining has become a key feature of the larger product. Data mining has grown from an isolated part of SQL Server Analysis Services with two algorithms, to an intrinsic part of the SQL Server Business Intelligence(BI) platform that is fully integrated with OLAP, Integration Services, andReporting Services. Other Microsoft applications (such as Microsoft Dynamix CRMand Microsoft Performance Point Server) seamlessly integrateSQLServer Data Mining to accentuate their functionality with predictive power. SQL Server Data Mining has become the most widely deployed data mining server in the industry, with many third-party software and consulting companies building on, specializing, and extending the platform. Enterprise,small and medium business, and even academic and scientific users have all adopted or switched to SQL Server Data Mining because of its scalability,availability, extensive functionality, and ease of use. This book serves as a guide to SQL Server Data Mining, explaining how it works, providing detailed technical and practical discussions of the SQL Server Data Mining technology, and demonstrating why you should deploy and use SQL Server Data Mining for yourself.


【文件预览】:
code
----Chapter4.zip(2.96MB)
----Appendix B.zip(6KB)
----Chapter5.xls(2.66MB)
----Chapter16.zip(343KB)
----Chapter8.zip(183KB)
----277744_ReadMe.txt(1KB)
----Chapter13.zip(5.71MB)
----Chapter9.zip(1.16MB)
----Chapter6.zip(169KB)
----Chapter12.zip(6.86MB)
----Chapter14.zip(4.3MB)
----Chapter10Projects.zip(204KB)
----Chapter3.zip(150KB)
----Chapter18.zip(1KB)
----Chapter11.zip(1.95MB)
----AppendixA.zip(23.46MB)
----Chapter7.zip(1.81MB)
----Chapter15.zip(254KB)

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  • 对于大数据非常有用。
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