PDF Ebook Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

PDF Ebook Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

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Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

PDF Ebook Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

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About the Author

Bruce Ratner, DM STAT-l Consulting

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Product details

Hardcover: 542 pages

Publisher: CRC Press; 2 edition (December 19, 2011)

Language: English

ISBN-10: 1439860912

ISBN-13: 978-1439860915

Product Dimensions:

6.2 x 1.2 x 9.5 inches

Shipping Weight: 1.9 pounds (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

12 customer reviews

Amazon Best Sellers Rank:

#1,704,278 in Books (See Top 100 in Books)

The best part of the book is the easy going writing style and story telling.However:It is very repetitive. It would ~20% shorter if verbatim passages were removed. Many ideas are restated throughout.It is a relatively expensive book. I expected more technical and complete coverage for the money.It does not adequately cover algorithms, mathematical concepts, or how to completely run the analysis described.It barely covers SAS implementation.It is not well organized. It comes across as a series of lectures or blog posts, in contrast to being a coherent book.

Mr. Ratner is one, ahem, passionate marketer -a. I first learnt of him and his GenIQ software from the e-mails he was posting to a LinkedIn group on a daily basis.b. Watch "Statistical modeling and analysis for database marketing" become "Statistical and machine-learning data mining" in its second edition - the first time I see a book go into (n+1) edition with a completely different title - adding three "hot" search terms: "data mining", "machine learning" and "big data". And it works (on some people) - the book has zilch to say about big data, yet here it is in the "Big data" section of a ("prestigious", according to Mr. Ratner) list published by IBM.c. He posts a five-star rating of own book - anonymously, as "The Significant Statistician". Two more reviews are from fans, who earlier gave five stars to the first edition, and both decided to upgrade; in total, three of the seven five-star reviews (excluding Mr. Ratner's own) come from his home state, NY, which is an interesting geographical spike. With one exception, the positive reviews have 2-3 positive votes each; coincidentally, my own review got two negative votes on the day of posting; literally putting two and two (multiplied by seven? or eight?) together, I can make a guess about where the votes are coming from.Mr. Ratner is also a bona fide statistics PhD, but it seems that in the decades after leaving grad school, he has not invested time in keeping his knowledge of the field up to date - or in writing his books, which are just another channel of GenIQ promotion. I have reviewed the first edition - take a look at the comments under that 2009 review - and am disappointed to see the second one just as poorly written (a half-page passage shows up, unchanged, three times - on pp. 18, 48, 90 - labor of love, you say?), poorly typeset and visually ugly, and, well, shallow.If this is your first book on statistical and non-statistical methods of data analysis, you may well be impressed, but at $80, the wisdom is a tad overpriced, and why not get a proper book by a recognized author? (I recommend "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani; "Doing data science" by Schutt and O'Neil is another, very different option). America has a proud tradition of garage inventors, but this one needs to spend more time in the garage.

One of the nicest books I have read in the recent past.This was recommend to me by my former mentor.I am so glad I took his advice and bought this book.I can say I have read 60% of the book and I plan to read the rest.Recommended!

I think it is a nice book. That does a very excellent job of explaining data mining from two perspectives: Statistics and machine learning.

I used this book as a resource to understand logistic regression better; and found much more. The author has a good balance of math and explanation.

Dr Ratner's overview of these topics hits some sweetspots with me. I'm always looking to understand trends in analytic tools and techniques that can provide my company with a competitive advantage. The probem I find in up and coming tools/techniques, is you need to be the esoteric statistician or mathematacian in the particular field to grasp the capabilities, let alone, how to use them. This book is much more positively utlitarian in design. It provides plenty of examples and allows someone with a basic understanding of scientific or engineering statistics access to machine learning and data mining techniques. This is a great text for: 1)those in business or NGO's exploring "Big Data" analysis; 2) manager's determining buget allocations for tools (which I think this book shows you don't necessarily need enterprise sofware packages); or 3) a survey course on these two topics. And some added bonuses: the author writes in an easy to read style and is more than willing to answer questions or have discussions on the topics.

Dr. Ratner has written a unique book that distinguishes between statistical and machine-learning data mining. The book includes 14 statistical data mining and 17 machine-learning data mining techniques. All techniques are quite practical, making this volume a handbook for every statistician, data miner, and machine-learner.Let me describe a few chapters that present approaches and techniques that I really favored.Chapter 3 introduces a new data mining method: a smoother scatterplot based on CHAID.Chapter 4 shows the importance of straight data for the simplicity and desirability it brings for good model building.Chapter 5 introduces the method of symmetrizing ranked data for upgrading nominal data to interval data, a nice trick of the trade.Chapter 13 provides a quantitative criterion for assessing competing predictive models and the importance of the predictor variables.Chapter 23 uses a metrical modelogue, "To Fit or Not to Fit Data to a Model," to introduce the machine-learning method of GenIQ and its favorable data mining offshoots.Chapter 24 maintains that the machine-learning paradigm, which lets the data define the model, is especially effective with big data.Chapter 25 introduces and illustrates the quintessential data mining concept: data reuse. Data reuse is appending new variables, which are found when building a GenIQ Model, to the original dataset. The benefit of data reuse is apparent: The original dataset is enhanced with the addition of new, predictive-full GenIQ data mined variables.I highly recommend this book by Dr. Ratner, and the knowledge gained will undoubtedly make me a better data miner.

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