Description

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.

Key Features

  • 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
  • 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

Readership

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

Table of Contents

Preface
Forwards (Dean Abbott and Tony Lachenbruch)
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
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 s

Details

No. of pages:
864
Language:
English
Copyright:
© 2009
Published:
Imprint:
Academic Press
Electronic ISBN:
9780080912035
Print ISBN:
9780123747655

About the authors

Robert Nisbet

Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years’ experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, 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 Insurance, Banking, and Credit industries. 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), and a co-author of "Practical Text Mining" (Academic Press, 2012). Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certification Program, teaching online courses in Effective Data preparation, and co-teaching Introduction to Predictive Analytics.

Gary Miner

Dr. Gary Miner received a B.S. from Hamline University, St. Paul, MN, 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 pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease. In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer’s disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Overall, Dr. Miner’s career has focused on medicine and health issues, so serving as the ‘project director’ for this current book on ‘Predictive Analytics of Medicine – Healthcare Issues’ fit his knowledge and skills perfectly. Gary also serves as VP & Scientific Director of Healthcare Predictive Analytics Corp; as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment

John Elder

Dr. John Elder heads the United States’ leading data mining consulting team, with offices in Charlottesville, Virginia; Washington, D.C.; and Baltimore, Maryland (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.

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)