Data Mining Applications with R.

Format:

Book

Author/Creator:

Zhao, Yanchang.

Contributor:

Cen, Yonghua.

Language:

English

Subjects (All):

Data mining--Industrial applications.

Data mining - Industrial applications.

Physical Description:

1 online resource (493 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2013.

Contents:

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

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.

2.6.2. Creating Document-Term Matrices

2.6.3. Loading Existing Term-Document Matrices

2.6.4. Running the Program

2.6.4.1. Comparing Models

2.7. Conclusions

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?

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

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

4.3.3. Exploring the Document-Term Matrix

4.4. Clustering Content by Topics Using the LDA

4.4.1. The Latent Dirichlet Allocation

4.4.2. Learning the Various Distributions for LDA

4.4.3. Using the Log-Likelihood for Model Validation

4.4.4. Topics Representation

4.4.5. Plotting the Topics Associations

4.5. Using Similarity Between Documents to Explore Document Cohesion

4.5.1. Computing Similarities Between Documents

4.5.2. Using a Heatmap to Illustrate Clusters of Documents

4.6. Social Network Analysis of Authors

4.6.1. Constructing the Network as a Graph

4.6.2. Author Importance Using Centrality Measures

4.7. Conclusion

References.

Chapter 5: Recommender Systems in R

5.1. Introduction

5.2. Business Case

5.3. Evaluation

5.4. Collaborative Filtering Methods

5.5. Latent Factor Collaborative Filtering

5.6. Simplified Approach

5.7. Roll Your Own

5.8. Final Thoughts

Chapter 6: Response Modeling in Direct Marketing: A Data Mining-Based Approach for Target Selection

6.1. Introduction/Background

6.2. Business Problem

6.3. Proposed Response Model

6.4. Modeling Detail

6.4.1. Data Collection

6.4.2. Data Preprocessing

6.4.2.1. Data Integration and Cleaning

6.4.2.2. Data Normalization

6.4.3. Feature Construction

6.4.3.1. Target Variable Construction

6.4.3.2. Predictor Variables

6.4.3.3. Interaction Variables

6.4.4. Feature Selection

6.4.4.1. F-Score

6.4.4.2. Step1: Selection of Interaction Features Using F-Score

6.4.4.3. Step2: Selection of Features Using F-Score

6.4.4.4. Step3: Selection of Best Subset of Features Using Random Forest

6.4.5. Data Sampling for Training and Test

6.4.6. Class Balancing

6.4.7. Classifier (SVM)

6.5. Prediction Result

6.6. Model Evaluation

6.7. Conclusion

Chapter 7: Caravan Insurance Customer Profile Modeling with R

7.1. Introduction

7.2. Data Description and Initial Exploratory Data Analysis

7.2.1. Variable Correlations and Logistic Regression Analysis

7.3. Classifier Models of Caravan Insurance Holders

7.3.1. Overview of Model Building and Validating

7.3.2. Review of Four Classifier Methods

7.3.3. RP Model

7.3.4. Bagging Ensemble

7.3.5. Support Vector Machine

7.3.6. LR Classification

7.3.7. Comparison of Four Classifier Models: ROC and AUC

7.3.8. Model Comparison: Recall-Precision, Accuracy-v-Cut-off, and Computation Times

7.4. Discussion of Results and Conclusion.

Appendix A. Details of the Full Data Set Variables

Appendix B. Customer Profile Data-Frequency of Binary Values

Appendix C. Proportion of Caravan Insurance Holders vis-à-vis other Customer Profile Variables

Appendix D. LR Model Details

Appendix E. R Commands for Computation of ROC Curves for Each Model Using Validation Dataset

Appendix F. Commands for Cross-Validation Analysis of Classifier Models

Chapter 8: Selecting Best Features for Predicting Bank Loan Default

8.1. Introduction

8.2. Business Problem

8.3. Data Extraction

8.4. Data Exploration and Preparation

8.4.1. Null Value Detection

8.4.2. Outlier Detection

8.5. Missing Imputation

8.5.1. Relevance Analysis

8.5.2. Data Set Balancing

8.5.3. Feature Selection

8.6. Modeling

8.7. Model Evaluation

8.8. Finding and Model Deployment

8.9. Lessons and Discussions

Appendix. Selecting Best Features for Predicting Bank Loan Default

Chapter 9: A Choquet Integral Toolbox and Its Application in Customer Preference Analysis

9.1. Introduction

9.2. Background

9.2.1. Aggregation Functions

9.2.2. Choquet Integral

9.2.3. Fuzzy Measure Representation

9.2.4. Shapley Value and Interaction Index

9.3. Rfmtool Package

9.3.1. Installation

9.3.2. Toolbox Description

9.3.3. Preference Analysis Example

9.4. Case Study

9.4.1. Traveler Preference Study and Hotel Management

9.4.2. Data Collection and Experiment Design

9.4.3. Model Evaluation

9.4.4. Result Analysis

9.4.4.1. Preference Profile Construction

9.4.4.2. Interaction Behavior Analysis

9.4.5. Discussion

9.5. Conclusions

Chapter 10: A Real-Time Property Value Index Based on Web Data

10.1. Introduction

10.2. Housing Prices and Indices

10.3. A Data Mining Approach

10.3.1. Data Capture.

10.3.2. Geocoding

10.3.3. Price Evolution

10.4. Real Estate Pricing Models

10.4.1. Model 1: Hedonic Model Plus Smooth Term

10.4.2. Model 2: GWR Plus a Smooth Term

10.4.3. Relationship to Other Work

10.5. Conclusion

Chapter 11: Predicting Seabed Hardness Using Random Forest in R

11.1. Introduction

11.2. Study Region and Data Processing

11.2.1. Study Region

11.2.2. Data Processing of Seabed Hardness

11.2.3. Predictors

11.3. Dataset Manipulation and Exploratory Analyses

11.3.1. Features of the Dataset

11.3.2. Exploratory Data Analyses

11.4. Application of RF for Predicting Seabed Hardness

11.5. Model Validation Using rfcv

11.6. Optimal Predictive Model

11.7. Application of the Optimal Predictive Model

11.8. Discussion and Conclusions

11.8.1. Selection of Relevant Predictors and the Consequences of Missing the Most Important Predictors

11.8.2. Issues with Searching for the Most Accurate Predictive Model Using RF

11.8.3. Predictive Accuracy of RF and Prediction Maps of Seabed Hardness

11.8.4. Limitations

Appendix AA. Dataset of Seabed Hardness and 15 Predictors

Appendix BA. R Function, rf.cv, Shows the Cross-Validated Prediction Performance of a Predictive Model

Chapter 12: Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage

12.1. Background

12.2. Challenges

12.3. Data Extraction and Exploration

12.4. Data Preprocessing

12.5. Modeling

12.6. Model Evaluation

12.7. Model Deployment

12.8. Lessons, Discussion, and Conclusions

Chapter 13: Crime Analyses Using R

13.1. Introduction

13.2. Problem Definition

13.3. Data Extraction

13.4. Data Exploration and Preprocessing

13.5. Visualizations

13.6. Modeling.

13.7. Model Evaluation.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Zhao, Yanchang Data Mining Applications with R

ISBN:

9780124115200

OCLC:

870340096