Design and Implementation of Data Mining Tools
Language: English
Published by Taylor & Francis Ltd, 2019
- Softcover
- Used
Condition: Used - Fine
Current price: US$ 61.03
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Zustand: Sehr gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | Based on the authors' own research work, this book explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Focusing on three applications of data mining, the book takes a practical approach to the subject. A hands-on guide, it 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.
Seller Inventory # 35525367/2
- Title
- Design and Implementation of Data Mining Tools
- Author
- Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang
- Publisher
- Taylor & Francis Ltd
- Publication year
- 2019
- Condition
- Sehr gut
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0367385554
- ISBN 13
- 9780367385552
- Seller catalogs
- Bücher
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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