New Data Mining Book Out

The new Nisbet, Elder, and Miner book is out now, and has been receiving good reviews on Amazon. A sampling of the 6 reviews so far (all 5 stars):

The "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.

This is an extraordinary book. So often within this field books are offered as bibles only to fall short. This book does not and delivers a wide array of information and useful tips for the beginner and veteran data miner.

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.

This is one of the few, of many, data mining books that delivers what it promises.

It has a great mix of data mining principles with step-by-step solutions (case studies) using data mining software, such as Clementine, Enterprise Miner and Statistica. It is this practical approach to data mining that fills a void in the current selection of books in the marketplace (and there are many great data mining books out there).

For some, the benefit of the book will be the case studies on Fraud Detection or Text MIning. For others, seeing how to solve problems using Enterprise Miner (or Clementine or Statistica) will be of most benefit, operating almost like a users manual. I most appreciated the first chapter on the history of statistics (Nisbet), Model Complexity and Ensembles (Elder) and the 10 Data Mining Mistakes (Elder).

One more quote, this from the second forward in the book:

This volume is not a theoretical treatment of the subject -- the authors themselves recommend other books for this -- but rather contains a description of data mining principles and techniques in a series of “knowledge-transfer” sessions, where examples from real data mining projects illustrate the main ideas. This aspect of the book makes it most valuable for practitioners, whether novice or more experienced.


The Handbook of Statistical Analysis and Data Mining Applications is 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

Can you learn data mining in undergraduate or graduate school?

I was recently asked by a former student from one of my data mining courses if a particular program was a good one to learn data mining (it happened to be this one, from NC State). It raises an interesting question: how much can data mining be learned from a book or a course?

Some of the best data miners I have met did not have any statistics course in their past, nor (for some) any higher level mathematics. For my part, I was a computational mathematics major undergrad, and applied math for my masters, but never took a stats course either (though I did take and TA a probability course). That stated, I always recommend in my courses that folks become familiar with basic statistics; one book I have recommended is linked in the book recommendations section--The Cartoon Guide to Statistics. Since I have never taken a college or graduate data mining course, I can't comment directly. My concern is that they are too theoretical (how the algorithms work) rather than practical (how to handle data problems, how to pose proper questions to be addressed by data mining, etc.).

I'm willing to be persuaded though, so if you have experience with good, practical data mining curricula, please let me know.

Another Moneyball quote

Gotta get back in the habit of posting...

A quick way is to post another quote from Moneyball that I really liked

Intelligence about baseball statistics had become equated in the public mind with the ability to recite arcane baseball stats. What James's wider audience had failed to understand was that the statistics were beside the point. The point was understanding; the point was to make life on earth just a bit more intelligible; and that point, somehow, had been lost. "I wonder," James wrote, "if we haven't become so numbed by all these numbers that we are no longer capable of truly assimilating any knowledge which might result from them."

(p.95)

What I like about this quote is that it is something may of us in the analytics world have experienced: losing the point of the modeling or summary statistics by forgetting why we are doing the analysis in the first place. Or, as my good friend John Elder used to describe it, "rapture of the depths"

Random things...

I was just looking at my favorite economics blog, The Skeptical Optimist, and saw a post on randomness based on two books the blog author, Steve Conover is reading called The Black Swan and Fooled by Randomness. This caught my eye--a quote from one of the two books (it was unclear to me which one):

Here's an example of his point about randomness: How many times have you heard about mutual fund X's "superlative performance over the last five years"? Our typical reaction to that message is that mutual fund X must have better managers than other funds. Reason: Our minds are built to assign cause-and-effect whenever possible, in spite of the strong possibility that random chance played a big role in the outcome.

He then gives an example of two stock pickers, one of whom gets it "right" about 1/2 the time, and a second who gets it right 12 consecutive times. The punch line is this:

Taleb's point: Randomness plays a much larger role in social outcomes than we are willing to admit—to ourselves, or in our textbooks. Our minds, uncomfortable with randomness, are programmed to employ hindsight bias to provide retroactive explanations for just about everything. Nonetheless, randomness is frequently the only "reason" for many events.

I personally don't agree philosophically with the role of randomness (I would prefer to say that many outcomes are unexplained then say randomness is the "reason" or "cause"--randomness does nothing itself, it is our way of saying "I don't know why" or "it is too hard to figure out why").

But that said, this is an extremley important principal for data miners. We have all seen predictive models that apparently do well on one data set, and then does poorly on another. Usually this is attributed to overfit, but it doesn't have to be solely an overfit problem. David Jensen of UMass described in one paper the phenomenon of oversearching for models in the paper Multiple Compisons in Induction Algorithms, where you could happen upon a model that works well, but is just a happenstance find.

The solution? One great help in overcoming these problems is through sampling--the train/test/validate subset method, or by resampling methods (like bootstrapping). But having the mindset of skepticism about models helps tremendously in digging to ensure the models truly are predictive and not just a random matching of the patterns of interest.