2009 HandbookOfStatAnalysis

• (Nisbet et al., 2009) ⇒ Robert Nisbet, John F. Elder, and Gary Miner. (2009). “Handbook of Statistical Analysis and Data Mining Applications.” Academic Press. ISBN:0123747651

Subject Headings: Data Mining Task, Data Mining Algorithm, Reference Book.

Quotes

Book Overview

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 helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book 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 industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book

Table of Contents

• 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
  • 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 aMEDICAL INDUSTRY
  • CLINICAL PSYCHOLOGY FNDING PREDICTORS OF CORRECT DIAGNOSIS
  • EDUCATION LEADERSHIP TRAINING: for BUSINESS and EDUCATION
• 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
  • Chapter 22: Summary,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 HandbookOfStatAnalysis	John F. Elder Robert Nisbet Gary Miner			Handbook of Statistical Analysis and Data Mining Applications			http://books.google.com/books?id=U5np34a5fmQC			2009