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

By Jian Pei, Jiawei Han, Micheline Kamber, et al.

Book Overview

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data-- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

• Format: Hardcover
• Language: English
• ISBN: 1558609016
• ISBN13: 9781558609013
• Release Date: January 2006
• Publisher: Morgan Kaufmann
• Length: 800 Pages
• Weight: 3.61 lbs.
• Dimensions: 9.3" x 1.3" x 7.5"

Customer Reviews

Significant improvements since first edition

I have read the first edition of this book years before. This second edition has significant improvements. Core topic (classification, clustering, association rules) is very detailed and much easier to read. The author also add much material about advanced topics such as graph mining, multimedia mining, stream and time series mining, etc. Although these advanced topics are not as well writen as core topics, at least you will...

Best introduction I know

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...

Deep, comprehensive and practical + data mining software

I learned about Data Mining - Concepts and Techniques from a friend who is a CS professor. He is using this book to teach his graduate and undergraduate classes and he said that the same book is also used by many leading universities such as Cornell, UC Berkeley, Georgia Tech.First I thought this book would be hard for me to follow because I do not have a degree in CS, and I just wanted to have a good comprehensive understanding...

A good textbook on the technical aspects of data mining

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...