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Mark Nelson

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It's Not Your Father's Plagiarism

February 28, 2010

This week I received a not atypical email from Amazon.com:

As someone who has purchased or rated JPEG2000: Image Compression Fundamentals, Standards and Practice (The International Series in Engineering and Computer Science) by David Taubman or other books in the Algorithms > Compression category, you might like to know that Vector Quantization: Quantization, Signal Processing, Data Compression, Centroid, K-Means Clustering, Clustering, Density Estimation, Self-Organizing Map, Simulated Annealing, Entropy Encoding is now available.  You can order yours for just $54.00 by following the link below.

You probably get emails like this all the time, and if you are like me, every once in a while you actually get a referral that looks pretty interesting. This book got my attention, because I'm a sucker for entropy encoding, K-means clustering, data compression, and quantization.

But as I looked at the email, the first thing I thought was "what's up with that crazy title?" It's like they created it by concatenating the chapter names into one huge mess.

Still, it was worth a click to see what this book was all about. I went to the page and was kind of stunned by the product description:

High Quality Content by WIKIPEDIA articles! Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensioned data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model.

It appears that this is a book written by robots that suck related Wikipedia articles into chapters, slap an ISBN number on the result, and then publish it.

I checked out Betascript Publishing, listed as the publisher of this book, and sure enough, it looks like they have turned this into a working business. If I click on the author's name, Lambert M. Surhone, Amazon returns almost 13,000 links!

It seems clear that Betascript has automated the process, prints the books on demand, and makes pure profit on any copies  they actually sell. But is anyone going to actually buy a book that is nothing more than reprints of Wikipedia pages?

Remind me to check back in a year and see if Betascript is still in business. I have no idea whether they are actually going to be able to pull this off.

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