- Hardcover: 425 pages
- Publisher: A Bradford Book; 1 edition (Aug 17 2001)
- Language: English
- ISBN-10: 026208290X
- ISBN-13: 978-0262082907
- Product Dimensions: 23.7 x 21.1 x 3.3 cm
- Shipping Weight: 1.1 Kg
- Average Customer Review: 3.7 out of 5 stars See all reviews (13 customer reviews)
- Amazon Bestsellers Rank: #419,965 in Books (See Top 100 in Books)
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Product Details |
Product Description
Book Description
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.
About the Author
David J. Hand is Professor of Statistics, Department of Mathematics, Imperial College, London. Heikki Mannila is Research Fellow at Nokia Research Center and Professor, Department of Computer Science and Engineering, Helsinki University of Technology. Padhraic Smyth is Associate Professor, Department of Information and Computer Science, the University of California, Irvine.
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Customer Reviews
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Most helpful customer reviews
5.0 out of 5 stars
Great book with a great layout!,
This review is from: Principles of Data Mining (Hardcover)
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.
5.0 out of 5 stars
A wonderful book but not a cookbook,
By
This review is from: Principles of Data Mining (Hardcover)
I am a professional data miner (20 yrs. experience) and data mining can be a treacherous business compared to conventional statistical analysis. There are many software packages that offer the novice a seemingly plethora of "information-extracting" tools. There is a tendency in the field to regard one or another of these as the final and eternal answer to a particular objective. This is the best guide so far in assisting the novice data miner in avoiding dumb mistakes and selecting the strongest analytical tool suited to data structure and objectives.This book can be read and understood by anyone who has had a decent basic course in statistics or or in pattern recognition. It alerts the reader to potential pitfalls in using a particular data mining procedure. It also clearly describes essential differences between procedures. Examples from real data are clear and integrated with the text. This is not a "cookbook" that teaches you keystroke by keystroke how to implement an algorithm. Instead this book is a guide in understanding the fundamentals behind each procedure (as good as possible assuming low level math skills), and hints on interpetation of output, especially limits to interpretation. It is very well written and can stand alone as a guide or serve as a testbook in a data mining class. Now if they would just write a book on bayesian decision-making in the same way.
1.0 out of 5 stars
This is NOT a Data Mining Book .. But a bad statistics book,
By Mustafa (Palestine, Gaza Strip) - See all my reviews
This review is from: Principles of Data Mining (Hardcover)
Finally .. I recevie the book .. I read the list of content and I surprised about it .. and now I know why they dont write the contents here to read before bying the book ..This is a bad statistics book, you can read any thing in it except about Data Mining ... No Cluster Analysis .. No Nural Networks .. No Rule induction No Dicecion Trees .. Nothing and nothing and nothing ... And I want to sell this bad book which Name is Data Mining ... for the three lier writers. Mustafa Ebaid
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