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The essential professional reference for data mining applications and statistical analysis

Table of Contents

Preface
Forwards
Introduction

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
Tutorials

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

GLOSSARY of STATISICAL and DATA MINING TERMS
INDEX
CD – With Additional Tutorials, data sets, Power Points, and Data Mining software

About the Author

Dr. Nisbet was trained initially in ecosystems analysis. He has over 30 years of experience in complex systems analysis and modeling as a researcher (University of California, Santa Barbara). He entered business in 1994 to lead the team that developed the first data mining models of customer response for AT&T and NCR Corporation. While at NCR Corporation and Torrent Systems, he pioneered the design and development of configurable data mining applications for retail sales forecasting and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications and Insurance. In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations; business intelligence reporting; and data quality analyses. He is lead author of the Handbook of Statistical Analysis & Data Mining Applications (Academic Press, 2009). Currently, he functions as a data scientist and independent data mining consultant. Dr. John Elder heads the United States' leading data mining consulting team, with offices in Charlottesville, Virginia; Washington, D.C.; Baltimore, Maryland; and Manhasset, New York (www.datamininglab.com) Founded in 1995, Elder Research, Inc. focuses on investment, commercial, and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market sector timing, and fraud detection. John obtained a B.S. and an M.E.E. in electrical engineering from Rice University and a Ph.D. in systems engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 16 years at ERI, he spent five years in aerospace defense consulting, four years heading research at an investment management firm, and two years in Rice's Computational & Applied Mathematics Department. Dr. Gary Miner received a B.S. from Hamline University, St. Paul, Minnesota, with Biology, Chemistry, and Education majors; an M.S. in Zoology and Population Genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA predoctoral fellowship. During the doctoral study years, he also studied mammalian genetics at the Jackson Laboratory, Bar Harbor, Maine, under a College Training Program on an NIH award; another College Training Program at the Bermuda Biological Station, St. George's West, Bermuda, in a Marine Developmental Embryology course, on an NSF award; and a third College Training Program held at the University of California, San Diego, at the Molecular Techniques in Developmental Biology Institute, again on an NSF award. Following that he studied as a postdoctoral student at the University of Minnesota in behavioral genetics, where, along with research in schizophrenia and Alzheimer's disease, he learned what was involved in writing books from assisting in editing two book manuscripts of his mentor Irving Gottesman, Ph.D.

Reviews

"...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)