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Data mining applications with R / Yanchang Zhao, Senior Data Miner, RDataMining.com, Australia, Yonghua Cen, Associate Professor, Nanjing University of Science and Technology, China

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Amsterdam ; Boston : Elsevier, [2014]Edition: Online-AusgDescription: Online-Ressource (1 online resource (xxi, 470 pages)) : illustrationsISBN:
  • 9781306167796
  • 1306167795
  • 9780124115200
Subject(s): Additional physical formats: 9780124115200 | 9780124115118 | 1306172454 | Erscheint auch als: Data mining applications with R. Druck-Ausgabe Amsterdam : Academic Press, 2014. XXI, 470 S.DDC classification:
  • 006.3/12 23
  • 006.312
RVK: RVK: ST 250 | ST 601LOC classification:
  • QA76.9.D343
Online resources: Summary: 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 Expression -- 1.3.3.2.3. 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 Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization -- 2.1. 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.PPN: PPN: 807201499Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE
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