Discovering Knowledge in Data
An Introduction to Data Mining
1st Edition
Published on 18. November 2004
Book
Hardback
240 pages
978-0-471-66657-8 (ISBN)
Description
Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
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.
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
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.
Reviews / Votes
"...an 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 way...case 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 novices...it 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) "...an excellent a white--boxa 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 Reviews.com, February 15, 2005)More details
Edition
1., Auflage
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Illustrations
ill
Dimensions
Height: 24.1 cm
Width: 16.1 cm
Thickness: 20 mm
Weight
518 gr
ISBN-13
978-0-471-66657-8 (9780471666578)
Schweitzer Classification
Other editions
New editions
Book
07/2014
2nd Edition
Wiley
€92.50
Shipment within 10-20 days
Person
DANIEL T. LAROSE received his PhD in statistics from the University of Connecticut. An associate professor of statistics at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world's first online master of science program in data mining. He has also worked as a data mining consultant for Connecticut-area companies. He is currently working on the next two books of his three-volume series on Data Mining: Data Mining Methods and Models and Data Mining the Web: Uncovering Patterns in Web Content, scheduled to publish respectively in 2005 and 2006.
Content
Preface.
1. An Introduction to Data Mining.
2. Data Preprocessing.
3. Exploratory Data Analysis.
4. Statistical Approaches to Estimation and Prediction.
5. k-Nearest Neighbor.
6. Decision Trees.
7. Neural Networks.
8. Hierarchical and k-Means Clustering.
9. Kohonen networks.
10. Association Rules.
11. Model Evaluation Techniques.
Epilogue: "We've Only Just Begun".
Index.
1. An Introduction to Data Mining.
2. Data Preprocessing.
3. Exploratory Data Analysis.
4. Statistical Approaches to Estimation and Prediction.
5. k-Nearest Neighbor.
6. Decision Trees.
7. Neural Networks.
8. Hierarchical and k-Means Clustering.
9. Kohonen networks.
10. Association Rules.
11. Model Evaluation Techniques.
Epilogue: "We've Only Just Begun".
Index.