Data mining applications with R

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Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. Twenty different real-world case studies illustrate various techniques in rapidly growing areas, in…

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Title Statements

Full title :Data mining applications with R / Yanchang Zhao, Yonghua Cen.

Summary

Edition Statements

Publication Date:©2014

Publication Statements

Publisher(s):Academic Press, Academic Press, an imprint of Elsevier
Place of Publication:Amsterdam
Language:eng
Publication Date:©2014

Notes

Title from title screen.

The Physical Object

Description:1 online resource (1 volume) : : illustrations

Edition Identifiers

ISBN:9780124115200, 0124115209, 012411511X, 9780124115118
oclc:on1058168513
LC Classification:QA76.9.D343
Dewey Decimal:006.3/12

Front Cover; Data Mining Applications with R; Copyright; Contents; Preface; Background; Objectives and Significance; Target Audience; Acknowledgments; Review Committee; Additional Reviewers; Foreword; References; Chapter 1: Power Grid Data Analysis with R and Hadoop; 1.1. Introduction; 1.2. A Brief Overview of the Power Grid; 1.3. Introduction to MapReduce, Hadoop, and RHIPE; 1.3.1. MapReduce; 1.3.1.1. An Example: The Iris Data; 1.3.2. Hadoop; 1.3.3. RHIPE: R with Hadoop; 1.3.3.1. Installation; 1.3.3.2. Iris MapReduce Example with RHIPE; 1.3.3.2.1. The Map Expression.

1.3.3.2.2. The Reduce Expression1.3.3.2.3. Running the Job; 1.3.3.2.4. Looking at Results; 1.3.4. Other Parallel R Packages; 1.4. Power Grid Analytical Approach; 1.4.1. Data Preparation; 1.4.2. Exploratory Analysis and Data Cleaning; 1.4.2.1. 5-min Summaries; 1.4.2.2. Quantile Plots of Frequency; 1.4.2.3. Tabulating Frequency by Flag; 1.4.2.4. Distribution of Repeated Values; 1.4.2.5. White Noise; 1.4.3. Event Extraction; 1.4.3.1. OOS Frequency Events; 1.4.3.2. Finding Generator Trip Features; 1.4.3.3. Creating Overlapping Frequency Data; 1.5. Discussion and Conclusions; Appendix; References.

Chapter 2: Picturing B…

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