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Other titles in the Chapman & Hall/CRC Data Mining and Knowledge Discovery series:
Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery)by Luis Torgo
Synopses & Reviews
The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.
Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:
With these case studies, the author supplies all necessary steps, code, and data.
A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.
Book News Annotation:
Appropriate for students of statistics interested in learning practical application of the R statistical data modeling computer program, this volume provides five case studies in the use of R for data mining in a variety of situations. Beginning with an introduction outlining the basic syntax and use of R, the work presents case studies in data mining to predict algae blooms, to predict stock market returns, for detecting fraudulent transactions and for classifying microarray samples. Chapters include descriptions of the problem, descriptions of the data set, loading the data into R, and specific analysis of the data mining tasks required, the R programming required and the results of the study. The volume includes numerous illustrations and code samples and access to online resources, including all of the code and data sets used in the case studies, is provided. Torgo is a professor of computer science at the University of Porto, Portugal. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)
This book provides a self-contained introduction to the use of R for exploratory data mining and machine learning. Employing a practical, learn-by-doing approach, the author presents a series of representative case studies from ecology, financial prediction, fraud detection, and bioinformatics, including all of the necessary steps, code, and data. These examples demonstrate how to address important data mining issues, such as handling data sets with too many variables, and illustrate key concepts, including outlier detection and semisupervised learning. A supporting web page provides additional code and data for further study.
"This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code"--
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