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Handbook of Statistical Analysis and Data Mining Applications 1st Edition
- 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 using Statistica, SAS and SPSS software
- 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
- ISBN-100123747651
- ISBN-13978-0123747655
- Edition1st
- PublisherAcademic Press
- Publication dateJune 5, 2009
- LanguageEnglish
- Dimensions7.75 x 1.5 x 9.25 inches
- Print length864 pages
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Editorial Reviews
Review
- Peter Lachenbruch, Oregon State U., Past President, American Statistical Society (from Foreword 1)
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)
... a valuable resource... The book's straightforward style--it presents intuitive explanations and avoids rigorous mathematical formulations--makes difficult concepts accessible to a broad audience.-ACM Computing Reviews
"...an exceptional book that should be on every data miner's bookshelf, or better yet, found lying open next to the computer."
Dean Abbott, Abbott Analytics --from Foreword 2
"...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)
Review
From the Back Cover
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 practical solutions. Using the methods of this book produces new KNOWLEDGE that facilitates DECISIONS which lead to ACTIONS that result in SUCCESS.
About the Author
Product details
- Publisher : Academic Press
- Publication date : June 5, 2009
- Edition : 1st
- Language : English
- Print length : 864 pages
- ISBN-10 : 0123747651
- ISBN-13 : 978-0123747655
- Item Weight : 3.65 pounds
- Dimensions : 7.75 x 1.5 x 9.25 inches
- Best Sellers Rank: #2,775,473 in Books (See Top 100 in Books)
- #1,966 in Statistics (Books)
- #3,723 in Probability & Statistics (Books)
- Customer Reviews:
About the authors
Gary D. Miner, Ph.D.
Miner.Gary@gmail.com
Dr. Gary Miner received his B.S. from Hamline University, St. Paul, Minnesota with biology, chemistry and education majors; M.S. in Zoology & Population Genetics from the University of Wyoming, and his Ph.D. in Biochemical Genetics from the University of Kansas as the recipient of a NASA Pre-Doctoral Fellowship. During the doctoral study years, he also studied mammalian genetics at The Jackson Laboratory, Bar Harbor, ME, under a College Training Program on an NIH award; and 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 Post-Doctoral student at the University of Minnesota in Behavioral Genetics, where, along with research in schizophrenia and Alzheimer’s Disease, he learned “how to write books” from assisting in editing two book manuscripts of his mentor, Irving Gottesman, Ph.D. (Dr. Gottesman returned the favor 41 years later by writing two tutorials for this PRACTICAL TEXT MINING book). After academic research and teaching positions, Dr. Miner did another two year NIH-Post-Doctoral in Psychiatric Epidemiology and Biostatistics at the University of Iowa where he became thoroughly immersed in studying affective disorders and Alzheimer’s Disease. All together he spend over 30 years researching and writing papers and books on the genetics of Alzheimer’s Disease (Miner, G.D., Richter, R, Blass, J.P., Valentine, J.L, and Winters-Miner, Linda. FAMILIAL ALZHEIMER’S DISEASE: Molecular Genetics and Clinical Perspectives. Dekker: NYC, 1989; and Miner, G.D., Winters-Miner, Linda, Blass, J.P., Richter, R, and Valentine, J.L. CARING FOR ALZHEIMER’S PATIENTS: A Guide for Family & Healthcare Providers. Plenum Press Insight Books: NYC. 1989). Over the years he held positions, including professor and chairman of a department, at various universities including The University of Kansas, The University of Minnesota, Northwest Nazarene University, Eastern Nazarene University, Southern Nazarene University, Oral Roberts University Medical School where he was Associate Professor of Pharmacology and Director of the Alzheimer Disease & Geriatric Disorders Research Laboratories, and even for a period of time in the 1990’s was a visiting Clinical Professor of Psychology for Geriatrics at the Fuller Graduate School of Psychology & Fuller Theological Seminary in Pasadena, CA. In 1985 he and his wife, Dr. Linda Winters-Miner [author of several tutorials in this book] founded The Familial Alzheimer’s Disease Research Foundation [aka “The Alzheimer’s Foundation] which became a leading force in organizing both local and international scientific meetings and thus bringing together all the leaders in the field of genetics of AD from several countries, which then lead to the writing of the first scientific book on the genetics of Alzheimer’s Disease; this book included papers by over 100 scientists coming out of the First International Symposium on the Genetics of Alzheimer’s Disease held in Tulsa, OK in October, 1987. During part of this time he was also an Affiliate Research Scientist with the Oklahoma Medical Research Foundation located in Oklahoma City with the University of Oklahoma School of Medicine. Dr. Miner was influential in bringing all of the world’s leading scientists working on Genetics of AD together at just the right time when various laboratories from Harvard to Duke University and University of California-San Diego, to the University of Heidelberg, in Germany, and universities in Belgium, France, England and Perth, Australia were beginning to find “genes” which they thought were related to Alzheimer’s Disease. During the 1990’s Dr. Miner was appointed to the Oklahoma Governor’s Task Force on Alzheimer’s Disease, and also Associate Editor for Alzheimer’s Disease for THE JOURNAL OF GERIATRIC PSYCHIATRY & NEUROLOGY, which he still serves on to this day. By 1995 most of these dominantly inherited genes for AD had been discovered, and the one that Dr. Miner had been working on since the mid-1980’s with the University of Washington in Seattle was the last of these initial 5 to be identified, this gene on Chromosome 1 of the human genome. At that time, having met the goal of finding out some of the genetics of AD, Dr. Miner decided to do something different, to find an area of the business world, and since he had been analyzing data for over 30 years, working for StatSoft, Inc. as a Senior Statistician and Data Mining Consultant seemed a perfect “semi-retirement” career. Interestingly (as his wife had predicted) , he discovered that the “business world” was much more fun than the “academic world”, and at a KDD-Data Mining meeting in 1999 in San Francisco, he decided that he would specialize in “data mining”. Incidentally, he first met Bob Nisbet there who told him, “You just have to meet this bright young rising star John Elder!”, and within minutes Bob found John introduced me to him, as he was also at this meeting. As Gary delved into this new “data mining” field, and looked at statistics text books in general, he saw the need for ‘practical statistical books’ and started writing chapters, and organizing various outlines for different books. Gary, Bob and John kept running into each other at KDD meetings, and eventually at a breakfast meeting in Seattle in August of 2005 decided they needed to write a book on data mining, and right there re-organized Gary’s outline which eventually became the book Handbook of Statistical Analysis and Data Mining Applications, 2009, published by Elsevier. And now, this interest has brought you, the reader, the book you are holding in your hands, PRACTICAL TEXT MINING. Gary has additional chapters, some drafts written in the early 2000’s, still unpublished, that most likely will be updated and become part of maybe at least two more books, one on medicine, health care delivery, and epidemiology, and another on use of predictive data analysis for education. All thanks to Dr. Irving Gottesman, Gary’s “mentor in book writing”, who planted the seed back in 1970 while Gary was doing a post-doctoral with him at the University of Minnesota.
Dr. John Elder heads the US's most experienced data mining consulting team, with offices in Charlottesville, Virginia, Washington DC, Baltimore MD, and Raleigh NC (www.elderresearch.com). Founded in 1995, Elder Research, Inc. focuses on Federal, commercial, investment, and security applications of advanced analytics, including text mining, stock selection, image recognition, biometrics, process optimization, cross-selling, drug efficacy, credit scoring, risk management, and fraud detection.
John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 20 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice's Computational & Applied Mathematics department.
Dr. Elder has been named one of the "10 most influential people in Analytics". He's authored innovative data mining tools, is a frequent keynote speaker, and has chaired international Analytics conferences. John's courses on analysis techniques -- taught at dozens of universities, companies, and government labs -- are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by President Bush to guide technology for National Security. His book on practical Data Mining, with Bob Nisbet and Gary Miner, won the PROSE award for top book in 2009 in Mathematics. He was one of the discoverers of the powers of ensemble modeling, and co-authored a book on it with Giovanni Seni in February 2010. His book on Practical Text Mining, with colleague Andrew Fast and 4 others, won the PROSE award for top book in 2012 in Computation and Information Science.
John is grateful to be a follower of Christ and the father of 5.
Robert (Bob) Nisbet, Ph.D.
Instructor
Predictive Analytics Certificate Program
University of California, Irvine
Bob2@rnisbet.com
Dr. Nisbet’s original training was in Plant Ecology. He taught and conducted research in Botany and Ecology for many years in several colleges and universities, most recently as a Researcher in Forest Growth Modeling at the University of California, Santa Barbara. For the last 20 years of his career, Bob was active as a Data Scientist, initially for AT&T, then for NCR Corporation (after the split in 1996). He led the Yield Management analytical team at NCR Corporation which pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications. He has worked also in Insurance, Banking, Credit, membership organizations (e.g. AAA), and Health Care industries.
He is lead author of the award-winning “Handbook of Statistical Analysis & Data Mining Applications” (Academic Press, 2009, 2017), and a co-author and general editor of the award-winning "Practical Text Mining" (Academic Press, 2012) and “Practical Predictive Analytics and Decisioning Systems in Medicine” (Academic Press, 2015). Currently, his new book on Effective Data Preparation is in press with Cambridge University Press.
In his retirement, he serves as an Instructor in the University of California at Irvine Predictive Analytics Certificate Program, teaching many online and on-campus courses each year in Effective Data Preparation and Predictive Analytics Applications. He serves also as a technical advisor of the Predictive Analytics Certificate Program at UC-Irvine, as a Technical Editor of the Practical Predictive Analytics series of books by Cambridge University Press.
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Customers find the book provides a good overview of data mining techniques with helpful examples. Moreover, they appreciate its ease of use, with one customer noting the step-by-step tutorials, and its readability, with one describing it as brilliantly written.
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Customers appreciate the book's comprehensive coverage of data mining techniques and helpful examples, with one customer noting how it embeds these methods in a broader analytical context.
"...critical to successful analytics, the automation in KXEN is an effective adjunct and accelerator to the expression of the analyst's intellectual..." Read more
"...It promises many detailed examples and cases. The companion DVD has detailed cases and also has a real 90 day trial copy of Statistica...." Read more
"Good read, touches upon broader aspects of data mining without delving too much into the mathematics of it. Good for any one interested in Data mining" Read more
"...that leads to the need for data mining; I found this bit of history quite fascinating and enlightening; it is something I've found in few other data..." Read more
Customers find the book easy to follow, with one customer noting it includes step-by-step tutorials and another mentioning practical hands-on advice.
"...I found KXEN's ability to automate the process (and leverage a database engine!)..." Read more
"...prefer RapidMiner, a product that is amazingly feature-rich, so easy to use it is actually fun, supported by a robust open-source model, and..." Read more
"...Through tutorials and case studies, anyone from those just learning about data mining to experts will get great value from this book...." Read more
"...It is a good for review and exploration of methods to augment the methods that I am most familiar with...." Read more
Customers find the book easy to read, with one describing it as brilliantly written.
"...With e download text easy read on pc screen not certain would be good read on K." Read more
"...The best chapter is the one of Top 10 DM mistakes. This is brilliantly written and edited...." Read more
"Well written, easy read" Read more
Top reviews from the United States
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- Reviewed in the United States on February 11, 2010Nisbet's, Elder's and Miner's book on statistical analysis, was effective in that it:
- gave a intellectual insight into the thinking world of the analyst, as well as
- defined a strong analytics process to follow and
- gave you hands on examples and tool discussions that attempt to implement the intellectual concepts and process.
In the cases of KXEN and SPSS's Clementine. I was able to download their trial software and use this book to exercise the concepts.
I found KXEN's ability to automate the process (and leverage a database engine!) and do much of the analytics at the "click of a button" very powerful, reinforcing much of the book.
It is clear that the "thinking" part of analytics is still important and critical to successful analytics, the automation in KXEN is an effective adjunct and accelerator to the expression of the analyst's intellectual thinking.
- Reviewed in the United States on June 23, 2009This is one of the few, of many, data mining books that delivers what it promises. It promises many detailed examples and cases. The companion DVD has detailed cases and also has a real 90 day trial copy of Statistica. I have taught data mining for over 10 years and I know it is very difficult to find comprehensive cases that can be used for classroom examples and for students to actually mine data. The price of the book is also very reasonable expecially when you compare the quantity and quality of the material to the typical intro stat book that usually costs twice as much as this data mining book.
The book also addresses new areas of data mining that are under development. Anyone that really wants to understand what data mining is about will find this book infinetly useful.
- Reviewed in the United States on March 14, 2014Good read, touches upon broader aspects of data mining without delving too much into the mathematics of it. Good for any one interested in Data mining
- Reviewed in the United States on June 3, 2010This book is for practitioners, not for those seeking a deeper understanding of data mining. It both makes and delivers on that claim. All major data mining topics are covered, though in a necessarily shallow manner in keeping with the book's goal of getting past the theory and moving to the practice.
Oddly, the very start of the book does have a bit of theory in the form of the historical roots of statistics and the limitations of statistics that leads to the need for data mining; I found this bit of history quite fascinating and enlightening; it is something I've found in few other data mining books, and I've read several.
The trouble is, I do like theory a bit. I have a master's in computer science, so I'm a bit biased that way, thus my relatively low scoring of the work.
About 1/3rd of the book is dedicated to working through real problems, and that is the overwhelming strength of the book. If you are one who learns by doing rather than by theorizing, you'll find this book outstanding.
The biggest criticism I have of the book is that it is clear that there are significant parts where the authors just didn't have their hearts in it; it felt like they wrote certain sections because the publisher told them they had to in order to hit some type of target marketing segment.
It's also unfortunate that all three software products provided expire in 90 days or less. I'm never one to accomplish anything in 90 days, let alone get through a 700-page technical work!!! I know they are the 3 top mining tools, but I much prefer RapidMiner, a product that is amazingly feature-rich, so easy to use it is actually fun, supported by a robust open-source model, and free.
Overall, a solid work. But to me, theory matters, that's one star down; and rigorous, enthusiastic writing matters, so that's two stars down. In the 3-stars that remain is lots of hands-on practice if you don't mind expiring software, and for that it is very strong.
- Reviewed in the United States on March 12, 2010discussion proceeds in logical way- start by working one example then go back and get your questions answered. trail software download no problems. highly recommend but must buy hardcopy to get dvd. dvd not included in e download. Amazon very quick to help me cancel e download and order hardcopy once i became aware the content of the dvd not included in e download. complements to author and thanks to Amazon for working problem. With e download text easy read on pc screen not certain would be good read on K.
- Reviewed in the United States on June 27, 2009I had experience with many of the statistical tools that fall under the heading of data mining. There are good books on GAMs and so on. What I like about this book is that it embeds those methods in a broader context, that of the philosophy and structure of data mining writ large, especially as the methods are used in the corporate world. To me, it was really helpful in thinking like a data miner, especially as it involves the mix of science and art.
I also had no experience with Statistica Data Miner but have been very impressed with the program relative to those that are less well documented (WEKA) and too darned expensive (SAS EM)
The richness of the examples is so helpful.
- Reviewed in the United States on August 23, 2010I thought from its title that this book not only comes with real world applications but also introduces basic concepts and theory behind these data mining algorithms. I was not asking for in-depth explanation for these algorithms but at least, it has to be able to make us comfortable with these methods. However, after reading these algorithms covered in this book, I still have no idea how these algorithms work and what is the best way to approach different methods. Certainly a big dissapointment for me!
Top reviews from other countries
- InvoiceReviewed in the United Kingdom on March 16, 2017
5.0 out of 5 stars Five Stars
brilliant material
- Trading CentralReviewed in Canada on September 28, 2013
5.0 out of 5 stars Eureka! Finally Datamining Reference for the Practitioner
It seems rare to find an actual book that lives up to it's description given by the authors as to the content and what can be expected when it comes to datamining topics. Generally the academic reviews are biased and favorable giving the practitioner a book that will be filled with proofs and formulas with little else to begin the implementation process.
This book comes as advertised, a handbook with practical explanations of techniques coupled with references to software that will give a "good enough" solution to a finance problem.
Others may complain that the book in fact is software specific but the topic area will require the use of sophisticated software and a supporting team to be of any value in the enterprise setting.
For the lone wolf the book still provides enough of an overview of the topic areas to allow the reader to be familiar with the buzzwords and concepts used to begin the process.
The authors have also kept the chapters short enough providing enough clarity in a few words without the clutter of academic textbook approaches that come with detailed bibliographies and references to obscure works that the practitioner has either no interest or desire to read as justification for the point being made.
As a reference book, this is the one that should be on the desk of the practitioner who can refer to it when the young gun who stumbles into the office speaking in incomprehensible terms that can easily confuse the manager and lead to lost time and effectiveness in presenting the findings given by the intern hired for the summer.
Nice to know that there are still writers with impeccable academic backgrounds writing books giving applied solutions for managers within a framework that uses generally available software packages as the learning vehicle.
Must have reference for the analytics and datamining field's that will play much greater roles in finance and business decision making.
- Mark SReviewed in the United Kingdom on October 26, 2017
1.0 out of 5 stars Needs serious editing
Far too long and repetitive.