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

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

Book Overview

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 1558604898
• ISBN13: 9781558604896
• Release Date: September 2000
• Publisher: Morgan Kaufmann
• Length: 550 Pages
• Weight: 2.66 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...