Implementing Data Mining Algorithms in Microsoft SQL Server
Author:   C. L. Curotto ,  N. F. F. Ebecken
Publisher:   WIT Press
Edition:   Illustrated edition
ISBN:  

9781845640378


Pages:   176
Publication Date:   31 March 2005
Format:   Mixed media product
Availability:   In Print   Availability explained
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Implementing Data Mining Algorithms in Microsoft SQL Server


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Overview

Data mining technology is growing rapidly and, in many industries, has become commonplace. The main reasons for this are advances in computer technology that acquire, store and retrieve enormous amounts of data about everything from everywhere. In 2000, Microsoft introduced a data mining feature in their Microsoft [Registered] SQL Server' 2000 Analysis Services. This book covers all the practicalities required to integrate a third-party data mining algorithm into SQL Server 2000. This book is designed for use by data mining researchers and information technology workers. It will also be an ideal text for IT master and doctorate courses.

Full Product Details

Author:   C. L. Curotto ,  N. F. F. Ebecken
Publisher:   WIT Press
Imprint:   WIT Press
Edition:   Illustrated edition
ISBN:  

9781845640378


ISBN 10:   1845640373
Pages:   176
Publication Date:   31 March 2005
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Mixed media product
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Chapter 1: Data mining technology overview The importance of data mining technology; Multidisciplinary aspects; The KDD process; Data preparation; Database aspects; Frequent approaches; Existing tools; Research activities; Applications; The future Chapter 2: Tools Hardware; Software Chapter 3: OLE DB for DM technology Universal data access architecture; OLE DB for DM specification Chapter 4: Implementation of DMclcMine The SNBi classifier; The clcMine data mining provider Chapter 5: Experimental results Waveform recognition problem; Meteorological data; Life insurance data; Performance study; Conclusions

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