Discovering Knowledge in Data: An Introduction to Data Mining / Edition 1

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Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.
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Editorial Reviews

From the Publisher
" excellent introductory book of data mining. I recommend it for every one who wants to learn data mining." (Journal of Statistical Software, May 2006)

"...selected material is described in a simple, clear, and…precise studies…examples, and screen shots has definitely added to the learning value of the book." (Journal of Biopharmaceutical Statistics, January/February 2006)

"...does a good job introducing data mining to skillfully previews some of the basic statistical issues needed to understand data mining techniques." (Journal of the American Statistical Association, December 2005)

"If you need a book to help colleagues understand your data mining procedures and results, this is the one you want to give them." (Technometrics, November 2005)

"…an excellent book…it should be useful for anyone interested in analysing epidemiological data." (Statistics in Medical Research, October 2005)

" excellent 'white-box' overview of established approaches for data analysis, in which readers are shown how, why, and when the methods work." (CHOICE, April 2005)

"Larose has the making of a good series of books on data mining…I, for one, look forward to the next two books in the series." (Computing, February 15, 2005)

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Product Details

  • ISBN-13: 9780471666578
  • Publisher: Wiley
  • Publication date: 11/28/2004
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 240
  • Sales rank: 1,478,671
  • Product dimensions: 6.46 (w) x 9.23 (h) x 0.69 (d)

Table of Contents

1 Introduction to data mining 1
2 Data preprocessing 27
3 Exploratory data analysis 41
4 Statistical approaches to estimation and prediction 67
5 k-nearest neighbor algorithm 90
6 Decision trees 107
7 Neural networks 128
8 Hierarchical and k-means clustering 147
9 Kohonen networks 163
10 Association rules 180
11 Model evaluation techniques 200
Epilogue : "we've only just begun" 215
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