Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications, Second Edition 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 helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitionersIncludes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build modelsContains practical advice from successful real-world implementationsBrings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutionsFeatures clear, intuitive explanations of novel analytical tools and techniques, and their practical applications INDICE: PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process 1. History - The Phases of Data Analysis throughout the Ages 2. Theory 3. The Data Mining Process 4. Data Understanding and Preparation 5. Feature Selection - Selecting the Best Variables 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 7. Basic Algorithms 8. Advanced Algorithms 9. Text Mining 10. Organization of 3 Leading Data Mining Tools 11. Classification Trees = Decision Trees 12. Numerical Prediction (Neural Nets and GLM) 13. Model Evaluation and Enhancement  14. Medical Informatics 15. Bioinformatics 16. Customer Response Models 17. Fraud Detection PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses Tutorials PART IV: Paradox of Complex Models; using the right model for the right use?, on-going development, and the Future 18: Paradox of Ensembles and Complexity 19: The Right Model for the Right Use 20: The Top 10 Data Mining Mistakes  21: Prospects for the Future - Developing Areas in Data Mining 22: Summary

• ISBN: 978-0-12-416632-5
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 864
• Fecha Publicación: 01/09/2017
• Nº Volúmenes: 1
• Idioma: Inglés