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Data mining applications with R /
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 diff...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Academic Press,
©2014.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000Ia 4500 | ||
|---|---|---|---|
| 001 | OR_ocn870340096 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 140212s2013 maua ob 001 0 eng d | ||
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| 020 | |a 9780124115118 | ||
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| 035 | |a (OCoLC)870340096 |z (OCoLC)867926520 | ||
| 037 | |a CL0500000382 |b Safari Books Online | ||
| 050 | 4 | |a QA76.9.D343 |b Z526 2013 | |
| 082 | 0 | 4 | |a 006.312 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Zhao, Yanchang, |d 1977- | |
| 245 | 1 | 0 | |a Data mining applications with R / |c Yanchang Zhao, Yonghua Cen. |
| 260 | |a Waltham, MA : |b Academic Press, |c ©2014. | ||
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |a 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 | |a 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 | |a 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 | |a 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 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining |x Industrial applications |v Case studies. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Exploration de données (Informatique) |x Applications industrielles |v Études de cas. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 655 | 7 | |a Case studies |2 fast | |
| 700 | 1 | |a Cen, Yonghua. | |
| 776 | 0 | 8 | |i Print version: |a Zhao, Yanchang, 1977- |t Data mining applications with R. |d Amsterdam ; Boston : Academic Press, an imprint of Elsevier, 2013 |z 9780124115200 |w (OCoLC)867631062 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780124115118/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL1574448 | ||
| 994 | |a 92 |b IZTAP | ||