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

Jiawei Han (Author), Micheline Kamber (Author)

Book Description

ISBN-10: 1558604898 | ISBN-13: 978-1558604896 | Publication Date: September 8, 2000 | Edition: 1st

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.

Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success.

Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.

Classroom Features Available Online:
- instructor's manual
- course slides (in PowerPoint)
- course supplementary readings
- sample assignments and course projects

* Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
* Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
* Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.

Editorial Reviews

Review

"many excellent features" -- Tony Jenkins,Vice Chairman, British Computer Society's, Data Management Specialist Group

Book Description

The Preeminent textbook and professional reference on data mining from the recognized authoirty on the subject.

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Product Details

• Hardcover: 550 pages
• Publisher: Morgan Kaufmann; 1st edition (September 8, 2000)
• Language: English
• ISBN-10: 1558604898
• ISBN-13: 978-1558604896
• Product Dimensions: 9.4 x 7.5 x 1.2 inches
• Shipping Weight: 2.6 pounds
• Average Customer Review: 3.6 out of 5 stars  See all reviews (24 customer reviews)
• Amazon Best Sellers Rank: #1,049,455 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

86 of 89 people found the following review helpful:

5.0 out of 5 stars A good textbook on the technical aspects of data mining, September 7, 2000

By 

Krishnan Pillaipakkamnatt - See all my reviews
(REAL NAME)   

This review is from: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)

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).

24 of 26 people found the following review helpful:

5.0 out of 5 stars Best introduction I know, November 14, 2004

By 

wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
(HALL OF FAME REVIEWER)    (TOP 500 REVIEWER)   

This review is from: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)

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

27 of 30 people found the following review helpful:

5.0 out of 5 stars Just right, November 15, 2000

By 

Matthew M. Shannon (Tampa, FL USA) - See all my reviews

Amazon Verified Purchase

This review is from: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)

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!