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R and data mining : examples and case studies

Author: Yanchang Zhao
Publisher: [Place of publication not identified] : Academic Press, ©2013.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book introduces using R for data mining. Data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research. Recently, there is an increasing tendency to do data mining with R, a free software environment for statistical computing and graphics. According to a poll by KDnuggets.com in early 2011, R is the 2nd popular tool for data mining work. By introducing  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Zhao, Yanchang, 1977-
R and data mining.
San Diego, CA : Academic Press, 2013
(DLC) 2012044335
(OCoLC)827722823
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yanchang Zhao
ISBN: 9780123972712 012397271X
OCLC Number: 821039505
Description: 1 online resource
Contents: Data import and export --
Data exploration --
Decision trees and random forest --
Regression --
Clustering --
Outlier detection --
Time series analysis and mining --
Association rules --
Text mining --
Social network analysis --
Case study 1. Analysis and forecasting of house price indices --
Case study 2. Customer response prediction and profit optimization --
Case study 3. Predictive modeling of big data with limited memory --
Online resources.
Responsibility: Yanchang Zhao.

Abstract:

Introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. This book provides practical methods for using  Read more...

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For example, many universities have courses on data mining, and the proposed book will be a useful reference for students learning data mining in those courses. There are also many training courses on data mining in industry, such as training by SAS and IBM on data mining. The book will be of interest to the course learners as well. Presents an introduction into using R for data mining applications, covering most popular data mining techniques. Provides code examples and data so that readers can easily learn the techniques. 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