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DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS
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Overview of DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS Book
| Title | DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS |
| Author | Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang, & 1 More |
| ISBN-13 | 9781420045901 |
| Publication | YESDEE |
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.
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Overview of DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS Book
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.
Book Type :
Software Design, Testing & Engineering
Book Binding :
Not Available
Language :
English
No. of Pages :
274 Pages
ISBN - 10 :
1420045903
ISBN - 13 :
9781420045901
DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS Published On :
18 June 2009
Publisher :
Auerbach Publications
Edition :
1st
About Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang, & 1 More:
Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang, & 1 More is the author of DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS.
Disclaimer:
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Overview of DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS Book
| Title | DESIGN AND IMPLEMENTATION OF DATA MINING TOOLS |
| Author | Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang, & 1 More |
| ISBN-13 | 9781420045901 |
| Publication | YESDEE |
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.