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!

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).

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

I like this book , thank u

Libro arrivato in ottime condizioni, pari al nuovo. Essendo di seconda mano ho risparmiato un bel po’. Come dice il titolo, il contenuto è indubbiamente molto ricco, apre ad argomenti approfondibili anche con altre risorse. Consigliato per chi si introduce alla materia.

I was referred by my classmate to go for this seller, I found that it was good for used book, only few markings but it really solves my purpose. No damage and we can trust this seller. Even the packaging was excellent.

It covers data mining really well, I used for my master's course and I've learned a lot from it.

Book is good in condition that i expected. Suggest everyone to buy this book