Handbook of Statistical Analysis and Data Mining Applications book cover

Handbook of Statistical Analysis and Data Mining Applications

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.

Business analysts, scientists, engineers, researchers, and students in statistics and data mining

Hardbound, 864 Pages

Published: May 2009

Imprint: Academic Press

ISBN: 978-0-12-374765-5


  • "...If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." - Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World “Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners.” -- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)


  • Preface
    Forwards (Dean Abbott and Tony Lachenbruch)

    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 CDPART 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

    CD - With Additional Tutorials, data sets, Power Points, and Data Mining software (STATISTICA Data Miner & Text Miner & QC-Miner - 90 day free trial)


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