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

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!

Good book. Lots of good foundational concepts.

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).

It is very easy to collect huge volumes of data - social statistics, bank records, biological data, and more - but very hard to pull useful facts out of the heap. This book is about processing large volumes of data in ways that let simple descriptions emerge. This is an introductory level book, aimed at someone with reasonably good programming skills. A little facility with statistics might help, but certainly isn't necessary. The book starts gently, with some very basic questions: what is data mining exactly, when there seem to be so many definitions for the term? What is a data warehouse, and how does it differ from a database? Next, the authors address the data itself in terms of quality, usability, and organization for efficient access. The central chapters, 4 thhrough 8, address various kinds of query specification, kinds of relationships to extract, correlations, clustering, and classification. None of the discussions is especially deep. All, however, are presented in pseudocode or simple math that can easily be translated into working code. The careful reader learns a few basic principles that work well in many contexts: entropy maximization, Bayesian analysis, and simple stats. It may be surprising to see how little of normal statistical analysis is used. I suspect the authors assume that stats-savvy readers will already know how to apply significance testing, and that stats-naive readers don't need the distraction. The last chapters discuss complex data, where the best structure for the data and the questions to be asked of it are not at all obvious, and tools and applications used in data mining. The book is nicely laid out as a textbook, with an orderly summary, problem set, and bibliography at the end of each chapter. The bibliography is more than just a list of names and authors - it actually helps the reader decide which references will give the best description of each of the chapter's topics. This is a clear, usable introduction to data mining: the data it uses, the questions it answers, and the techniques for connecting them. It gives codable detail for lots of techniques, and prepares the reader for more advanced discussions. I recommend it very highly. //wiredweird

Data Mining conjures up many myths - if you are interested in digging deeper and evaluate if data mining can server your business needs, start with this book. This book does not go into the depth that could assist readers in performing data mining. This is a starter book that prod in the right direction without any unnecessary fluff. Data mining is about observing and understanding the data - it is not a straight mathematical or scientific skill set. Specific algorithms can assist in data mining - a significant challenge, very often, is getting and preparing the data to get it to a state where data mining can be performed. This book focuses on the algorithmic side of the equation.

This book was essencial for me to dig deeper into data mining techniqes and methonds. It is a good guidance book for beginners and also for advanced practictioners and researchers. It covers systematically all major themes on data mining and provides additional references for briefly covered topics. My subject area is web mining. I found this book as an overview book. I could get a wide view of the field. I got good hints for my specific field. It is very strictly written book not preferring this or other products as several comercial books of today do. The book is very up to date. I would say it is a current bible of data mining science, though there could be some similar or even better books on the subject in last month but maybe I'm not up to date.

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 following chapter, 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.