Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 4th Edition

I've been working with Data Warehousing for a few years, and stumbled upon this book here on Amazon a few weeks ago. I was leery at first because of it's obvious textbook price/look, but purchased it anyway, much to my delight.The book provides a very vendor neutral view of Data Warehousing and Data Mining, many data mining ideas and examples are presented throughout the book without any specific programming language used. I feel it allows you to implement the idea in your preferred method.I found the book more than worth the price, in fact I was asked to give a guest lecture/presentation at a University Data Mining class in the Spring and will definitely pull from this book for my presentation.Enjoy!

Very reliable seller! The book arrived in time and in very good condition. Thanks a lot!

This book is a good introduction on Data Mining with solid explanations of the mathematics behind the methods.

There are a number of books on data mining. The vast majority of them are non-technical in the sense that they talk a great deal about how data mining is a glorious area, without ever getting into the nitty gritty of how data mining algorithms actually work. There are also a couple of technical textbooks on data mining that are nothing more than mistitled books on machine learning (yes, I know, the ML arena does contribute a lot towards data mining). This is the first true textbook on data mining algorithms and techniques. It covers a vast array of topics and does ample justice to the vast majority of them. In fact, it even covers semi-automated (OLAP) technologies for data mining. The book consistently uses data from a single (fictitious) organization to illustrate most concepts. This gives a strong sense of cohesion to can actually be very different techniques. One key aspect of the book is its question-and-answer format. The main arguments in favor of such a format are (1) it is a clean way introduce a new topic or concept (2) students love it when things are laid out for them. On the other hand, such an approach seems inappropriate for a graduate level text. This book is certain to become "the standard" data mining textbook.Update (Dec 25, 2004): My opinion about this book has changed over time. I've left the 5-start rating in place, although my current rating for the book is 4 (or even 3.5) stars. The main reason is that I had to supplement most of the chapters in the book with the original research papers to give my students a more complete picture of data mining (in other words, the material can be a bit shallow).

I was assigned this book as a textbook for a class. It wasn't a very useful book:- No answers to exercises, what is the point of having exercises if there are no answers to see if you did it right?- No solutions manual I could find, either.- Examples don't always "finish" a problem, they stop after the first or second step. Where's the full solution to the FP-Growth example?- The papers this book references are easier to understand than this text, and more detailed. I started just seeking out the referenced papers because they were easier to read and I learned more. The papers almost always had more complete examples and better explanations.- This book almost always deals in abstract concepts, even adding its own layers of abstraction that don't seem to be used anywhere else. I personally found this confusing, I would have preferred that he gave more practical examples using actual database systems (e.g., for relational databases: Oracle, MS SQL, etc.) rather than, for example how he invented his own query language. Surely there must have been something practical in use at the time that he could have referenced? I know this book is rather old but for me, even an outdated implementation would have been preferable to no practical reference.- Sometimes parts of this book are copied from these references without adding anything. Also, not enough effort was put in to edit the paper's terminology / naming conventions to match the rest of the book.- The writing in this book is so vague at times I'd read the same paragraph 3 times and get 3 different interpretations, while I get what I need from the papers immediately.- This book repeats itself, I think they talked about binning in detail (a quite simple concept) at least 3 or 4 times.- This book has a habit of introducing a simple topic in one chapter, re-introducing it in the next chapter, and re-re-introducing it in the followingchapter, to finally talk about it 2 chapters later. Chapters 1-3 did not help me at all because of this, and in my opinion there is a lot of filler material in this book.The subject matter presented in this book is not hard, but the presentation makes it difficult I would not recommend this book, but I would recommend the works it cites.