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Principles of Data Mining Hardcover – 15 October 2001

by David Hand (Author), Heikki Mannila (Author), Padhraic Smyth (Author)
4.1 out of 5 stars 12
Edition: 1st
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The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local 'memory-based' models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
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  1. ISBN-10
    026208290X
  2. ISBN-13
    978-0262082907
  3. Edition
    1st
  4. Publisher
    The MIT Press
  5. Publication date
    15 October 2001
  6. Language
    English
  7. Dimensions
    23.7 x 21.06 x 3.3 cm
  8. Print length
    578 pages
  9. See all details
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Product details

  • Publisher ‏ : ‎ The MIT Press; 1st edition (15 October 2001)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 578 pages
  • ISBN-10 ‏ : ‎ 026208290X
  • ISBN-13 ‏ : ‎ 978-0262082907
  • Reading age ‏ : ‎ 18 years and up
  • Dimensions ‏ : ‎ 23.7 x 21.06 x 3.3 cm
  • Customer Reviews:
    4.1 out of 5 stars 12

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  1. D. J. Hand

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Lynn Farrell
5.0 out of 5 stars Five Stars
Reviewed in the United Kingdom on 20 September 2017
Verified Purchase
Good price and prompt delivery
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Shankar Saikia
5.0 out of 5 stars GREAT INTRO TO DATA MINING
Reviewed in the United States on 14 January 2010
Verified Purchase
I bought this book because I wanted a relatively high level (not too high level, but high level enough to give me a good foundation in the theory and issues) to data mining. My goal was to first understand the theory and principles of data mining before getting into the technological and application specifics (e.g., how to use software such as Dataminer or R or Weka or SPSS Clementine etc.).

This book has met my goals. Most chapters include abstract math/statistics that may be a little challenging for people who do not have a recent high level undergraduate statistics background. Actually I enjoyed the math/stats, and did not worry about going too deep into those portions. Trust me, the abstract concepts are not easy to grasp beyond a certain point, but they are EXTREMELY valuable. I am really glad that I was challeged. If you want another perspective or intro to data mining you may want to read some of the lecture notes of the "Machine Learning" course from MIT's online courseware - the courses are available for free on MIT's online courseware site. The lecture notes are even more abstract - they will make you appreciate this book.

I highly recommend that anyone who wants to get an intro to data mining should first read this book. After reading this book the reader can read a book that explains a specific data mining software package such as "Intro to R" or "Data Mining: Practical Machine Learning Tools & Techniques" (by Witten and Frank, good if you want to learn Weka).
5 people found this helpful
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Kevin Nasman
5.0 out of 5 stars Great book with a great layout!
Reviewed in the United States on 14 March 2004
Verified Purchase
I'd been struggling with the seemingly infinite ways to approach data mining and this book cleared it all up for me. It is absolutely full of information and is a great base reference. It does not contain complete algorithms or step by step instructions (you can get those anywhere these days) but instead is a comprehensive survey of all the best known methods for data mining. I really like how the authors combined classical mining techniques with more modern ones (ex: Bayesian Networks). Other books try to stay in one camp or the other, all while denying that they use very similar sub-components.
This book is well worth it. I promise you will find more information than you could possibly retain.
9 people found this helpful
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Craig Woodworth
3.0 out of 5 stars not much to comment on really, 94% of IT ...
Reviewed in the United Kingdom on 18 February 2015
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not much to comment on really, 94% of IT is dead, meaning 94% of MD's have been kicked out for being fake.
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Sean Hemingway
5.0 out of 5 stars Excellent: covers statistics and data mining and their differences
Reviewed in the United States on 15 January 2017
Verified Purchase
Excellent: covers statistics and data mining and their differences.

I hope the authors are planning a new edition to match the changes in technology.
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