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LEADER 00000cam a2200673Ia 4500
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008 140212s2013 maua ob 001 0 eng d
019 867926520
020 9780124115200
020 0124115209
020 012411511X
020 9780124115118
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099 eBook O'Reilly for Public Libraries
100 1 Zhao, Yanchang,|d1977-|1https://id.oclc.org/worldcat/
entity/E39PCjFDJ7qFJkWXvty3FwjKVC
245 10 Data mining applications with R /|cYanchang Zhao, Yonghua
Cen.|h[O'Reilly electronic resource]
260 Waltham, MA :|bAcademic Press,|c©2014.
300 1 online resource (1 volume) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references and index.
505 0 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.
505 8 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.
505 8 Chapter 2: Picturing Bayesian Classifiers: A Visual Data
Mining Approach to Parameters Optimization2.1.
Introduction; 2.2. Related Works; 2.3. Motivations and
Requirements; 2.3.1. R Packages Requirements; 2.4.
Probabilistic Framework of NB Classifiers; 2.4.1. Choosing
the Model; 2.4.1.1. Multivariate Bernoulli model; 2.4.1.2.
Multinomial Model; 2.4.1.3. Poisson Model; 2.4.2.
Estimating the Parameters; 2.5. Two-Dimensional
Visualization System; 2.5.1. Design Choices; 2.5.2.
Visualization Design; 2.6. A Case Study: Text
Classification; 2.6.1. Description of the Dataset.
505 8 2.6.2. Creating Document-Term Matrices2.6.3. Loading
Existing Term-Document Matrices; 2.6.4. Running the
Program; 2.6.4.1. Comparing Models; 2.7. Conclusions;
Acknowledgments; References; Chapter 3: Discovery of
Emergent Issues and Controversies in Anthropology Using
Text Mining, Topic Modeling, and Social Ne ... ; 3.1.
Introduction; 3.2. How Many Messages and How Many Twitter-
Users in the Sample?; 3.3. Who Is Writing All These
Twitter Messages?; 3.4. Who Are the Influential Twitter-
Users in This Sample?; 3.5. What Is the Community
Structure of These Twitter-Users?
505 8 3.6. What Were Twitter-Users Writing About During the
Meeting?3.7. What Do the Twitter Messages Reveal About the
Opinions of Their Authors?; 3.8. What Can Be Discovered in
the Less Frequently Used Words in the Sample?; 3.9. What
Are the Topics That Can Be Algorithmically Discovered in
This Sample?; 3.10. Conclusion; References; Chapter 4:
Text Mining and Network Analysis of Digital Libraries in
R; 4.1. Introduction; 4.2. Dataset Preparation; 4.3.
Manipulating the Document-Term Matrix; 4.3.1. The Document
-Term Matrix; 4.3.2. Term Frequency-Inverse Document
Frequency.
520 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, including: RetailCrime and homeland securityStock
mark.
588 0 Print version record.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Data mining|xIndustrial applications|vCase studies.
650 0 R (Computer program language)
650 6 Exploration de données (Informatique)|xApplications
industrielles|vÉtudes de cas.
650 6 R (Langage de programmation)
650 7 R (Computer program language)|2fast
655 7 Case studies|2fast
700 1 Cen, Yonghua.
776 08 |iPrint version:|aZhao, Yanchang, 1977-|tData mining
applications with R.|dAmsterdam ; Boston : Academic Press,
an imprint of Elsevier, 2013|z9780124115200
|w(OCoLC)867631062
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9780124115118/?ar
|zAvailable on O'Reilly for Public Libraries
938 EBL - Ebook Library|bEBLB|nEBL1574448
994 92|bJFN