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Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems) Paperback – 6 Jan 2011

4.7 out of 5 stars 6 customer reviews

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Product details

  • Paperback: 664 pages
  • Publisher: Morgan Kaufmann; 3 edition (6 Jan. 2011)
  • Language: English
  • ISBN-10: 0123748569
  • ISBN-13: 978-0123748560
  • Product Dimensions: 19 x 3.8 x 23.5 cm
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Bestsellers Rank: 36,723 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

Review

"...offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations."

"Co-author Witten is the author of other well-known books on data mining, and he and his co-authors of this book excel in statistics, computer science, and mathematics. Their in- depth backgrounds and insights are the strengths that have permitted them to avoid heavy mathematical derivations in explaining machine learning algorithms so they can help readers from different fields understand algorithms. I strongly recommend this book to all newcomers to data mining, especially to those who wish to understand the fundamentals of machine learning algorithms."--INFORMS Journal of Computing

"The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project."--Book News, Reference & Research

"I would recommend this book to anyone who is getting started in either data mining or machine learning and wants to learn how the fundamental algorithms work. I liked that the book slowly teaches you the different algorithms piece by piece and that there are also a lot of examples. I plan on taking a machine learning course this upcoming fall semester and feel that the book gave me great insight that the course will be based on mathematics more than I had originally expected. My favorite part of the book was the last chapter where it explains how you can solve different practical data mining scenarios using the different algorithms. If there were more chapters like the last one, the book would have been perfect. This book might not be that useful if you do not plan on using the Weka software or if you are already familiar with the various machine learning algorithms. Overall, Data Mining: Practical Machine Learning Tools and Techniques is a great book to learn about the core concepts of data mining and the Weka software suite."-- ACM SIGSOFT Software Engineering Notes

"This book is a must-read for every aspiring data mining analyst. Its many examples and the technical background it imparts would be a unique and welcome addition to the bookshelf of any graduate or advanced undergraduate student. The book is written for both academic and application-oriented readers, and I strongly recommend it to any reader working in the area of machine learning and data mining."--Computing Reviews.com

About the Author

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.


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By John M. Ford TOP 1000 REVIEWER on 28 Feb. 2013
Format: Kindle Edition
This is a good text on machine learning techniques from both the statistics and the machine learning perspectives. The authors note that these fields have developed in parallel with many researchers and practitioners working in each, but few familiar with the full range of techniques in both disciplines. Some procedures, such as tree induction and nearest neighbor clustering techniques, have been developed independently in both fields. However, for the most part statistics has focused on hypothesis testing and machine learning has tried to optimize search through the space of possible hypotheses. This book presents techniques from both traditions.

The organizational structure of the book supports its use as either a comprehensive text or a modular reference. The first section's five chapters introduce the foundations of data mining. In addition to concepts and definitions, there are simple example data sets and accessible descriptions of how both raw data and final analyses are used in this field. A particularly well-written fifth chapter discusses how to evaluate data mining models. It discusses the rationale for holdout samples, the use of cross-validation procedures, and how to avoid over-fitting models. Machine learning texts frequently lack depth on this topic while statistics texts often fail to communicate the consequences of poorly-fitted models. This integration of perspectives is a good one.

Chapters in the second section build on this foundation. Chapter 6 describes how to use ten different techniques to detect and describe patterns in large data sets.
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Format: Paperback
This book is written by the software architects/developers of the WEKA machine learning tool. The book is large and comprehensive. The authors introduce the reader to the correct terminology to use when referring to concepts, and every concept they mention they follow through and explain its purpose and nuances in detail. The first 400 pages are dedicated to data mining and machine learning theory in an academic context with examples using simple tabular data which is easy to understand and is never longer than 1 page so as not to overcomplicate the learning experience (KISS - keep it simple stupid). There is however some maths that is over my head (the polynomials on page 231 and cartesian products on page 266). The remaining 200 pages are focused on using the WEKA multi-platform tool which the authors personally developed and have made open source. The hands-on section using WEKA also includes exercises which are suitable for a classroom environment. The authors of this book are not chancers looking to make some quick money, they are clearly experts and are very very good at what they do. Highly recommended.
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Format: Paperback Verified Purchase
This is a very well written and easy to read book on the topic of Data Mining, it is closely related to the Weka data mining software.

I use this book for a module at my University and it is also very reasonably priced (circa £21.00) which for a student book is affordable.

The book chapters follow a logical order, from data input, output representations, data mining algorithms for supervised (classification) and unsupervised (clustering) and examples for Weka. It covers all the major models, from Linear, Statistical, Rule representation and decision trees: It covers basic algorithms such as 1R (OneR) and various clustering methods, K-Means etc.

There is a few places where it is not clear or followed through in methods, I had to spend a bit of time replicating their numbers, some of the examples seem to be coincidental in their numbers which makes it more difficult to apply a technique to different data sets due to the some parts, lack of follow through (decision tree induction based on entropy as a splitting criteria for example, the book stops short of following through an example on deeper levels in the tree, this I had to replicate the numbers to understand the method).

K-nearest neighbour chapter is lacking depth, it does not seem to cover the process of creating Voronoi tessellation boundary diagrams just jumps into Kd-Trees and so on. Could have more introduction and more coverage of KNN.

Association Rule mining, the book is not really clear on support and confidence, at least not compared to other books on the subject that more clearly states the calculations. Could have better and more varied examples, to show the application of the methods with differing data sets to help clarify application of methods.

A worthwhile book on your bookshelf for machine learning applied to data mining.
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