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Record Nr. |
UNINA9910809843503321 |
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Titolo |
Design and implementation of data mining tools / / M. Awad. [et al.] |
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Pubbl/distr/stampa |
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Boca Raton, Fla. : , : Auerbach Publications, , 2009 |
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ISBN |
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0-367-38555-4 |
0-429-11670-5 |
1-282-11123-X |
9786612111235 |
1-4200-4591-1 |
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Descrizione fisica |
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1 online resource (276 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front cover; Dedication; Contents; Preface; About the Authors; Acknowledgments; Chapter 1. Introduction; Chapter 2. Data Mining Techniques; Chapter 3. Data Mining Applications; Chapter 4. Data Mining for Security Applications; Chapter 5. Dynamic Growing Self-Organizing Tree Algorithm; Chapter 6. Data Reduction Using Hierarchical Clustering and Rocchio Bundling; Chapter 7. Intrusion Detection Results; Chapter 8. Web Data Management and Mining; Chapter 9. Effective Web Page Prediction Using Hybrid Model; Chapter 10. Multiple Evidence Combination for WWW Prediction |
Chapter 11. WWW Prediction ResultsChapter 12. Multimedia Data Management and Mining; Chapter 13. Image Classification Models; Chapter 14. Subspace Clustering and Automatic Image Annotation; Chapter 15. Enhanced Weighted Feature Selection; Chapter 16. Image Classification and Performance Analysis; Chapter 17. Summary and Directions; APPENDIX A; Data Management Systems: Developments and Trends; Index; Back cover |
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Sommario/riassunto |
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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 |
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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 v |
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