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Detalles de la obra

Título:
Handbook of statistical analysis and data mining applications
Autor(es):
Nisbet, Robert; Elder, John; Miner, Gary
Pie de imprenta:
San Diego, California: Elsevier, c2009
Descripción física:
xxxiv, 210 p. diagrs., grafs., il. 24 x 20 cm.
Idioma:
Inglés
ISBN:
978-0-12-374765-5
Resumen:
Contiene: I. History of phases of data analysis, basic theory, and the data mining process. 1. The background for data mining practice. 2. Theoretical considerations for data mining. 3. The data mining process. 4. Data understanding and preparation. 5. Feature selection. 6. Accessory tools for doing data mining.- II. The algorithms in data mining and text mining, the organization of the three most common data mining tools, and selected specialized areas using data mining. 7. Basic algorithms for data mining: a brief overview. 8. Advanced algorithms for data mining. 9. Text mining and natural language processing. 10. The three most common data mining software tools. 11. Classification. 12. Numerical prediction. 13. Model evaluation and enhancement. 14. Medical informatics. 15. Bioinformatics. 16. Customer response modeling. 17. Fraud detection.- III. Tutorials- step by step case studies as a starting point to learn how to do data mining analyses.- IV. Measuring true complexity, the "right model for the right use", top mistakes, and the future of analytics. 18. Model complexity (and how ensembles help). 19. The right model for the right purpose: when less is good enough. 20. Top 10 data mining mistakes. 21. Prospects for the future of data mining and text mining as part of our everyday lives. 22. Summary: our design.-
Referencias bibliográficas:
Incluye Bibliografía
Ubicación física:
658.834 8 / NIS
Tipo de material:
[Material Impreso]