Handbook of statistical analysis and data mining applications /

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The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook he...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nisbet, Robert
Otros Autores: Elder, John F. (John Fletcher), Miner, Gary
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Academic Press/Elsevier, ©2009.
Temas:
Data mining > Statistical methods.
Exploration de données (Informatique) > Méthodes statistiques.
COMPUTERS > Reference.
COMPUTERS > Machine Theory.
COMPUTERS > Computer Literacy.
COMPUTERS > Information Technology.
COMPUTERS > Data Processing.
COMPUTERS > Computer Science.
COMPUTERS > Database Management > Data Mining.
COMPUTERS > Hardware > General.
Exploration de données > Méthodes statistiques.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
  • Chapter 1. History
  • The Phases of Data Analysis throughout the Ages
  • Chapter 2. Theory
  • Chapter 3. The Data Mining Process
  • Chapter 4. Data Understanding and Preparation
  • Chapter 5. Feature Selection
  • Selecting the Best Variables
  • Chapter 6: Accessory Tools and Advanced Features in Data
  • PART II:
  • The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
  • Chapter 7. Basic Algorithms
  • Chapter 8: Advanced Algorithms
  • Chapter 9. Text Mining
  • Chapter 10. Organization of 3 Leading Data Mining Tools
  • Chapter 11. Classification Trees = Decision Trees
  • Chapter 12. Numerical Prediction (Neural Nets and GLM)
  • Chapter 13. Model Evaluation and Enhancement
  • Chapter 14. Medical Informatics
  • Chapter 15. Bioinformatics
  • Chapter 16. Customer Response Models
  • Chapter 17. Fraud Detection
  • PART III: Tutorials
  • Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
  • Listing of Guest Authors of the Tutorials
  • Tutorials within the book pages:
  • How to use the DMRecipe
  • Aviation Safety using DMRecipe
  • Movie Box-Office Hit Prediction using SPSS CLEMENTINE
  • Bank Financial data
  • using SAS-EM
  • Credit Scoring
  • CRM Retention using CLEMENTINE
  • Automobile
  • Cars
  • Text Mining
  • Quality Control using Data Mining
  • Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data:
  • Business Administration in a Medical Industry
  • Clinical Psychology- Finding Predictors of Correct Diagnosis
  • Education
  • Leadership Training: for Business and Education
  • Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book
  • Listing of Tutorials on Accompanying CD
  • PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future.
  • Chapter 18: Paradox of Ensembles and Complexity
  • Chapter 19: The Right Model for the Right Use
  • Chapter 20: The Top 10 Data Mining Mistakes
  • Chapter 21: Prospect for the Future
  • Developing Areas in Data Mining.

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