Overview
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.
Full Product Details
Author: Robert Nisbet (Researcher, University of California, Irvine Predictive Analytics Certification Program, University of California, Santa Barbara, USA) , John Elder (Elder Research, Inc. and the University of Virginia, Charlottesville, USA) , Gary D. Miner (Retired, currently Board Member for and teaching with the University of California, Irvine Predictive Analytics Certificate Program, USA)
Publisher: Elsevier Science Publishing Co Inc
Imprint: Academic Press Inc
Dimensions:
Width: 19.10cm
, Height: 3.80cm
, Length: 23.50cm
Weight: 1.628kg
ISBN: 9780123747655
ISBN 10: 0123747651
Pages: 864
Publication Date: 19 August 2009
Audience:
Professional and scholarly
,
Professional & Vocational
Replaced By: 9780124166325
Format: Hardback
Publisher's Status: Out of Print
Availability: In Print 
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.
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
Reviews
Data mining practitioners, here is your bible, the complete driver's manual for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. 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 advise, 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)
Data mining practitioners, here is your bible, the complete driver's manual for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. 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 advise, 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)
Author Information
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.
Tab Content 6
Author Website:
Customer Reviews
Recent Reviews
No review item found!
Add your own review!
Countries Available
All regions
|
