Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)

By Dorian Pyle

Book Overview

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has... This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 1558605290
• ISBN13: 9781558605299
• Release Date: April 1999
• Publisher: Morgan Kaufmann
• Length: 560 Pages
• Weight: 2.18 lbs.
• Dimensions: 9.3" x 1.3" x 7.3"

Customer Reviews

Great book about data and Data Mining

The book "Data Preparation for Data Mining" is not a common Data Mining book. Nowdays a classical Data Mining book contains a metodology how to solve class of standard problems. This means more a set of prescriptions and receipts for elaborating various solutions to customer loyality, retention etc.The book by Dorian Pyle is different.It is not a Data Mining book, because as the authors claims, Data Mining is only a part of wider subject, which he calls Data Exploration. He shows us wider spectrum of various subjects considering data than you can find in other books. He gives us a good background that helps to recognize the source of problems with data. Some subjects are not to find in other sources. They come directly from author's reach experience. To summarise - the autor of the book managed to describe the whole subject area considering data, that is not to find in other books on this topics. To achive that knowledge we should read many other publications from different areas.

A must have book

Anyone who practices data mining lives the issues discussed in this book. The book dissects and explains important data challenges. Dorian communicates far more than knowledge about the nomenclature of the problems. His focus is instead on the trade-offs one makes while wrestling with data issues. He provides sage counsel on how the practioner can address data for the specific types of analytical problems one faces.I own about 15 data mining books, this is the one that I use the most.

This book saved me when all else had failed.

I was in the market for some information on how to scale data before using it with a neural network. After trying to wade through material that was somewhat inaccessible to my feeble brain, this book saved me. I was able to implement a simple scaling system in less than 2 hours. I later asked one of the econometricians at my company if I had done the scaling properly, and he said I did! This book is simple to understand, and best of all, it was correct!!

The true data miner toolkit

This book is simply great, it provides the best practices about the essence of data mining : data preparationAny analyst knows that 80% of data mining time is spent in data preparation, nevertheless most authors focus on the remaining 20% : techniques and tools, where the value is more "visible"The truth is that this value is unavaillable, unless you get data prepared, and that's the difficult jobDorian book's allows you to understand this obscure process through a great analytical process. Strongly recommended

A must have...

I've had the pleasure of listening to Dorian speak at seminars and even sharing a few brief words with him in person. When he mentioned to me last year that he was working on this book I had no idea how thorough and complete it would be. In fact, I remember wondering to myself how anyone could get their hands around this difficult, yet important aspect of data mining. I'm in awe! Anyone in the trenches will immediately understand the value of this book. Those just getting started in data mining will probably have no idea how much simpler their job just became. My only criticism of this book is that its title obscures that fact that there is a wealth of general data mining information contained within it - practical well beyond the data preparation phase. To understand why and how certain data preparation techniques work is to go a long way towards appreciating subtleties throughout the rest of the data mining process. Thanks Dorian!