Handbook of Statistical Analysis and Data Mining Applications
Academic Press Inc (Verlag)
978-0-12-374765-5 (ISBN)
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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.
Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling. 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. Dr. Gary Miner PhD 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. Gary was also co-author of “Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Overall, Dr. Miner’s career has focused on medicine and health issues, and the use of data analytics (statistics and predictive analytics) in analyzing medical data to decipher fact from fiction. Gary has also served as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in ‘Introduction to Predictive Analytics’, ‘Text Analytics’, ‘Risk Analytics’, and ‘Healthcare Predictive Analytics’ for the University of California-Irvine. Recently, until ‘official retirement’ 18 months ago, he spent most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dell’s acquisition of StatSoft (www.StatSoft.com) in April 2014). Currently Gary is working on two new short popular books on ‘Healthcare Solutions for the USA’ and ‘Patient-Doctor Genomics Stories’.
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
| Verlagsort | San Diego |
|---|---|
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 1470 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| ISBN-10 | 0-12-374765-1 / 0123747651 |
| ISBN-13 | 978-0-12-374765-5 / 9780123747655 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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