
Statistical Data Mining Using SAS Applications by George Fernandez
Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition, this resource describes statistical data mining concepts and methods and includes 13 user-friendly SAS macro applications for performing complete data mining tasks. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results.-
Data Mining with R
-
Biological Data Mining
-
Exploratory Data Analysis Using R
-
Advanced Data Science and Analytics with Python
-
Automated Data Analysis Using Excel
-
Privacy-Aware Knowledge Discovery
-
Computational Intelligent Data Analysis for Sustainable Development
-
Data Mining for Design and Marketing
-
Data Classification
-
Mining Software Specifications
-
Contrast Data Mining
-
Introduction to Computational Health Informatics
-
Social Networks with Rich Edge Semantics
-
Graph-Based Social Media Analysis
-
Industrial Applications of Machine Learning
-
Computational Business Analytics
-
Event Mining
-
Advances in Machine Learning and Data Mining for Astronomy
-
Human Capital Systems, Analytics, and Data Mining
-
Practical Graph Mining with R
-
Large-Scale Machine Learning in the Earth Sciences
-
Support Vector Machines
-
Geographic Data Mining and Knowledge Discovery
-
Demystifying AI
-
Data Science and Machine Learning for Non-Programmers
-
Knowledge Discovery from Data Streams
-
Data Science and Analytics with Python
-
RapidMiner, Second Edition
-
Feature Engineering for Machine Learning and Data Analytics
-
Text Mining and Visualization
-
Knowledge Guided Machine Learning
-
Healthcare Data Analytics
-
Text Mining
Its key features include the provision of case studies throughout the sections, downloadable macros and instructions on how to run them… The step-by-step instructions and the graphical representations of data make it particularly useful to those wishing to communicate complex and technical data to a largely non-specialist audiences.
—Kassim S. Mwitondi, Journal of Applied Statistics, 2012
Like the first edition of the book, this new edition provides a high-level introduction to some important concepts and algorithms in data mining. … the author presents broad statistical data mining solutions without writing SAS program codes. One of the nicest features of this book is that it gives access to SAS macros directly from the desktop and offers to create publication quality graphs. … this new edition provides a simple and straightforward introduction to data mining, along with a number of detailed, worked case studies.
—Technometrics, February 2011
Praise for the First Edition:The macros integrate nicely with SAS’s output delivery system … . this is a book that could serve as an easy-to read introduction to some classical statistical techniques that are used in data mining, and, with the associated macros, provide an opportunity to see those techniques in action.
—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466
Use of these data mining SAS macros facilitated reliable conversion, examination, and analysis of the data, and selection of best statistical models despite the great size of the data sets. …
—Christopher Ross, US Bureau of Land Management
An excellent treatment of data mining using SAS applications is provided in this book. … This book would be suitable for students (as a textbook), data analysts, and experienced SAS programmers. No SAS programming experience, however, is required to benefit from the book.
—Computing Reviews, June 2003
… the book provides a welcome contrast to treatments of data mining that focus on only the most novel aspects of the subject. Dr. Fernandez is quite right in pointing out that a lot of data mining can be carried out by standard statistical methods in familiar packages. The book also has a healthy emphasis on the use of cross validation (a hallmark of data mining). This and other concepts are well illustrated with numerous examples. Finally, the book demonstrates that the fancy (and expensive) user interfaces sported by many data mining work benches are not essential to the data mining enterprise and might even be counterproductive.
—Computational Statistics, 2005
Its key features include the provision of case studies throughout the sections, downloadable macros and instructions on how to run them. … The step-by-step instructions and the graphical representations of data make it particularly useful to those wishing to communicate complex and technical data to a largely non-specialist audiences.
—Kassim S. Mwitondi, Journal of Applied Statistics, 2012
If I had to recommend a good introduction to data mining, I would choose this one.
— J. A. Pardo, Complutense University of Madrid, Madrid, Spain, in Statistical Papers, 2012
Like the first edition of the book, this new edition provides a high-level introduction to some important concepts and algorithms in data mining. … the author presents broad statistical data mining solutions without writing SAS program codes. One of the nicest features of this book is that it gives access to SAS macros directly from the desktop and offers to create publication quality graphs. … this new edition provides a simple and straightforward introduction to data mining, along with a number of detailed, worked case studies.
—Technometrics, February 2011
Praise for the First Edition:The macros integrate nicely with SAS’s output delivery system … . this is a book that could serve as an easy-to read introduction to some classical statistical techniques that are used in data mining, and, with the associated macros, provide an opportunity to see those techniques in action.
—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466
Use of these data mining SAS macros facilitated reliable conversion, examination, and analysis of the data, and selection of best statistical models despite the great size of the data sets. …
—Christopher Ross, US Bureau of Land Management
An excellent treatment of data mining using SAS applications is provided in this book. … This book would be suitable for students (as a textbook), data analysts, and experienced SAS programmers. No SAS programming experience, however, is required to benefit from the book.
—Computing Reviews, June 2003
… the book provides a welcome contrast to treatments of data mining that focus on only the most novel aspects of the subject. Dr. Fernandez is quite right in pointing out that a lot of data mining can be carried out by standard statistical methods in familiar packages. The book also has a healthy emphasis on the use of cross validation (a hallmark of data mining). This and other concepts are well illustrated with numerous examples. Finally, the book demonstrates that the fancy (and expensive) user interfaces sported by many data mining work benches are not essential to the data mining enterprise and might even be counterproductive.
—Computational Statistics, 2005
George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.
| SKU | Unavailable |
| ISBN 13 | 9781439810750 |
| ISBN 10 | 1439810753 |
| Title | Statistical Data Mining Using SAS Applications |
| Author | George Fernandez |
| Series | Chapman And Hall Crc Data Mining And Knowledge Discovery Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | CRC Press |
| Year published | 2010-06-18 |
| Number of pages | 478 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
































