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Handbook of Statistical Analysis and Data Mining Applications

ISBN-10: 0123747651
ISBN-13: 9780123747655
Edition: 2009
List price: $97.95 Buy it from $24.69
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Description: The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book for scientists, engineers and researchers that brings together in a single resource all the information a beginner will need to rapidly  More...

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Book details

List price: $97.95
Copyright year: 2009
Publisher: Elsevier Science & Technology Books
Publication date: 5/22/2009
Binding: Hardcover
Pages: 864
Size: 7.50" wide x 9.25" long x 1.50" tall
Weight: 2.904
Language: English

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book for scientists, engineers and researchers that brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis required to interpret the data once mined. -One complete resource containing everything needed to successfuly conduct data mining analysis -historical perspective and background providing insight as to why/how data mining grew out of statistics, the scientific method and modern machine learning/artificial intelligence -Brief discreptions of the numerous new algorithms that make up the AI/Machine Learning part of the data mining arsenal, so the user new to data mining can rapidly absorb the ideas behind each method -Extensive case studies, most in a tutorial format, allowing the reader to 'click through' the example, using a software program, thus learning to conduct data mining analyses in the most rapid manner of learning possible Philosophical perspective and insight into what to expect in the future Numerous examples, tutorials, powerpoints and datasets available via the companion website Glossary of data mining terms provided in the appendix Four page color insert

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.

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 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 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', and 'Risk Analytics' for the University of California-Irvine, and other classes in medical predictive analytics for the University of California-San Diego; he spends 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 in April 2014).

Preface
Forwards
Introduction
History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
History - The Phases of Data Analysis throughout the Ages
Theory
The Data Mining Process
Data Understanding and Preparation
Feature Selection - Selecting the Best Variables
Accessory Tools and Advanced Features in Data
The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
Basic Algorithms
Advanced Algorithms
Text Mining
Organization of 3 Leading Data Mining Tools
Classification Trees = Decision Trees
Numerical Prediction (Neural Nets and GLM
Model Evaluation and Enhancement
Medical Informatics
Bioinformatics
Customer Response Models
Fraud Detection
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
Fnding Predictors of Correct Diagnosis Education
Leadership Training: for Business and Education
Many Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier
Web site for this book
Listing of Tutorials on Accompanying CD
Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future
Paradox of Ensembles and Complexity
The Right Model for the Right Use
The Top 10 Data Mining Mistakes
Prospect for the Future - Developing Areas in Data Mining
Summary GLOSSARY of Statisical and Data Mining Terms Index 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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