کاربردهای داده‌کاوی با 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 different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. The book* Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries * Presents various case studies in real-world applications, which will help readers to apply the techniques in their work * Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves **R code, Data and color figures for the book are provided at the RDataMining.com website.** Front Cover......Page 1 Data Mining Applications with R......Page 4 Copyright......Page 5 Contents......Page 6 Objectives and Significance......Page 14 Target Audience......Page 15 Acknowledgments......Page 16 Review Committee......Page 18 Additional Reviewers......Page 19 Foreword......Page 20 References......Page 22 1.1. Introduction......Page 24 1.2. A Brief Overview of the Power Grid......Page 25 1.3. Introduction to MapReduce, Hadoop, and RHIPE......Page 28 1.3.1.1. An Example: The Iris Data......Page 29 1.3.2. Hadoop......Page 30 1.3.3. RHIPE: R with Hadoop......Page 31 1.3.3.2. Iris MapReduce Example with RHIPE......Page 32 1.3.3.2.1. The Map Expression......Page 33 1.3.3.2.3. Running the Job......Page 34 1.3.3.2.4. Looking at Results......Page 35 1.3.4. Other Parallel R Packages......Page 36 1.4. Power Grid Analytical Approach......Page 37 1.4.1. Data Preparation......Page 38 1.4.2.1. 5-min Summaries......Page 39 1.4.2.2. Quantile Plots of Frequency......Page 41 1.4.2.3. Tabulating Frequency by Flag......Page 43 1.4.2.4. Distribution of Repeated Values......Page 44 1.4.2.5. White Noise......Page 46 1.4.3. Event Extraction......Page 48 1.4.3.1. OOS Frequency Events......Page 49 1.4.3.2. Finding Generator Trip Features......Page 50 1.4.3.3. Creating Overlapping Frequency Data......Page 51 1.5. Discussion and Conclusions......Page 54 Appendix......Page 55 References......Page 57 2.1. Introduction......Page 58 2.2. Related Works......Page 59 2.3. Motivations and Requirements......Page 60 2.3.1. R Packages Requirements......Page 61 2.4. Probabilistic Framework of NB Classifiers......Page 62 2.4.1. Choosing the Model......Page 63 2.4.1.2. Multinomial Model......Page 65 2.4.1.3. Poisson Model......Page 66 2.4.2. Estimating the Parameters......Page 67 2.5. Two-Dimensional Visualization System......Page 70 2.5.1. Design Choices......Page 71 2.5.2. Visualization Design......Page 72 2.6.1. Description of the Dataset......Page 75 2.6.2. Creating Document-Term Matrices......Page 76 2.6.3. Loading Existing Term-Document Matrices......Page 77 2.6.4.1. Comparing Models......Page 78 2.7. Conclusions......Page 82 References......Page 83 3.1. Introduction......Page 86 3.2. How Many Messages and How Many Twitter-Users in the Sample?......Page 88 3.3. Who Is Writing All These Twitter Messages?......Page 89 3.4. Who Are the Influential Twitter-Users in This Sample?......Page 90 3.5. What Is the Community Structure of These Twitter-Users?......Page 95 3.6. What Were Twitter-Users Writing About During the Meeting?......Page 98 3.7. What Do the Twitter Messages Reveal About the Opinions of Their Authors?......Page 103 3.8. What Can Be Discovered in the Less Frequently Used Words in the Sample?......Page 107 3.9. What Are the Topics That Can Be Algorithmically Discovered in This Sample?......Page 109 3.10. Conclusion......Page 111 References......Page 114 4.1. Introduction......Page 118 4.2. Dataset Preparation......Page 119 4.3.1. The Document-Term Matrix......Page 120 4.3.2. Term Frequency-Inverse Document Frequency......Page 122 4.3.3. Exploring the Document-Term Matrix......Page 123 4.4.1. The Latent Dirichlet Allocation......Page 124 4.4.2. Learning the Various Distributions for LDA......Page 125 4.4.3. Using the Log-Likelihood for Model Validation......Page 127 4.4.4. Topics Representation......Page 128 4.4.5. Plotting the Topics Associations......Page 129 4.5.1. Computing Similarities Between Documents......Page 131 4.6.1. Constructing the Network as a Graph......Page 132 4.6.2. Author Importance Using Centrality Measures......Page 136 References......Page 138 5.3. Evaluation......Page 140 5.4. Collaborative Filtering Methods......Page 141 5.5. Latent Factor Collaborative Filtering......Page 150 5.6. Simplified Approach......Page 166 5.7. Roll Your Own......Page 168 5.8. Final Thoughts......Page 172 References......Page 174 6.1. Introduction/Background......Page 176 6.2. Business Problem......Page 178 6.3. Proposed Response Model......Page 179 6.4.2. Data Preprocessing......Page 181 6.4.2.2. Data Normalization......Page 182 6.4.3.1. Target Variable Construction......Page 183 6.4.3.2. Predictor Variables......Page 184 6.4.3.3. Interaction Variables......Page 186 6.4.4. Feature Selection......Page 187 6.4.4.1. F-Score......Page 188 6.4.4.2. Step1: Selection of Interaction Features Using F-Score......Page 189 6.4.4.3. Step2: Selection of Features Using F-Score......Page 190 6.4.4.4. Step3: Selection of Best Subset of Features Using Random Forest......Page 191 6.4.5. Data Sampling for Training and Test......Page 192 6.4.6. Class Balancing......Page 194 6.4.7. Classifier (SVM)......Page 195 6.5. Prediction Result......Page 197 6.6. Model Evaluation......Page 198 6.7. Conclusion......Page 200 References......Page 201 7.1. Introduction......Page 204 7.2. Data Description and Initial Exploratory Data Analysis......Page 205 7.2.1. Variable Correlations and Logistic Regression Analysis......Page 207 7.3.1. Overview of Model Building and Validating......Page 208 7.3.2. Review of Four Classifier Methods......Page 211 7.3.3. RP Model......Page 213 7.3.4. Bagging Ensemble......Page 215 7.3.5. Support Vector Machine......Page 216 7.3.6. LR Classification......Page 218 7.3.7. Comparison of Four Classifier Models: ROC and AUC......Page 222 7.3.8. Model Comparison: Recall-Precision, Accuracy-v-Cut-off, and Computation Times......Page 224 7.4. Discussion of Results and Conclusion......Page 229 Appendix A. Details of the Full Data Set Variables......Page 232 Appendix B. Customer Profile Data-Frequency of Binary Values......Page 235 Appendix C. Proportion of Caravan Insurance Holders vis-à-vis other Customer Profile Variables......Page 243 Appendix D. LR Model Details......Page 245 Appendix F. Commands for Cross-Validation Analysis of Classifier Models......Page 248 References......Page 249 8.1. Introduction......Page 252 8.3. Data Extraction......Page 253 8.4.1. Null Value Detection......Page 254 8.4.2. Outlier Detection......Page 255 8.5.1. Relevance Analysis......Page 258 8.5.2. Data Set Balancing......Page 260 8.5.3. Feature Selection......Page 262 8.6. Modeling......Page 263 8.8. Finding and Model Deployment......Page 266 Appendix. Selecting Best Features for Predicting Bank Loan Default......Page 267 References......Page 268 9.1. Introduction......Page 270 9.2.1. Aggregation Functions......Page 271 9.2.2. Choquet Integral......Page 272 9.2.3. Fuzzy Measure Representation......Page 274 9.2.4. Shapley Value and Interaction Index......Page 275 9.3.1. Installation......Page 276 9.3.2. Toolbox Description......Page 277 9.3.3. Preference Analysis Example......Page 278 9.4.1. Traveler Preference Study and Hotel Management......Page 281 9.4.2. Data Collection and Experiment Design......Page 282 9.4.3. Model Evaluation......Page 283 9.4.4. Result Analysis......Page 286 9.4.4.1. Preference Profile Construction......Page 287 9.4.4.2. Interaction Behavior Analysis......Page 288 9.4.5. Discussion......Page 292 9.5. Conclusions......Page 293 References......Page 294 10.2. Housing Prices and Indices......Page 296 10.3. A Data Mining Approach......Page 297 10.3.1. Data Capture......Page 298 10.3.2. Geocoding......Page 300 10.3.3. Price Evolution......Page 303 10.4. Real Estate Pricing Models......Page 306 10.4.1. Model 1: Hedonic Model Plus Smooth Term......Page 307 10.4.2. Model 2: GWR Plus a Smooth Term......Page 310 10.4.3. Relationship to Other Work......Page 316 References......Page 318 11.1. Introduction......Page 322 11.2. Study Region and Data Processing......Page 323 11.2.2. Data Processing of Seabed Hardness......Page 324 11.2.3. Predictors......Page 327 11.3. Dataset Manipulation and Exploratory Analyses......Page 328 11.3.2. Exploratory Data Analyses......Page 329 11.4. Application of RF for Predicting Seabed Hardness......Page 330 11.5. Model Validation Using rfcv......Page 336 11.6. Optimal Predictive Model......Page 338 11.7. Application of the Optimal Predictive Model......Page 342 11.8.1. Selection of Relevant Predictors and the Consequences of Missing the Most Important Predictors......Page 344 11.8.2. Issues with Searching for the Most Accurate Predictive Model Using RF......Page 346 11.8.3. Predictive Accuracy of RF and Prediction Maps of Seabed Hardness......Page 347 11.8.4. Limitations......Page 348 Appendix BA. R Function, rf.cv, Shows the Cross-Validated Prediction Performance of a Predictive Model......Page 349 References......Page 350 12.1. Background......Page 354 12.2. Challenges......Page 355 12.3. Data Extraction and Exploration......Page 359 12.4. Data Preprocessing......Page 364 12.5. Modeling......Page 367 12.6. Model Evaluation......Page 371 12.7. Model Deployment......Page 378 12.8. Lessons, Discussion, and Conclusions......Page 382 Acknowledgments......Page 385 References......Page 386 13.1. Introduction......Page 390 13.2. Problem Definition......Page 391 13.4. Data Exploration and Preprocessing......Page 392 13.5. Visualizations......Page 398 13.6. Modeling......Page 408 13.7. Model Evaluation......Page 415 13.8. Discussions and Improvements......Page 417 References......Page 418 14.1. Introduction to the Case Study and Organization of the Analysis......Page 420 14.2. Background of the Analysis: The Italian Football Championship......Page 421 14.3.1. Data Extraction......Page 422 14.3.2. Data Exploration......Page 423 14.4.1. Variable Importance Evaluation......Page 426 14.4.2. Composite Indicators Construction......Page 431 14.4.2.1. PCA for the Home Team......Page 432 14.4.2.2. PCA for the Away Team......Page 434 14.5. Model Development: Building Classifiers......Page 435 14.5.1. Learning Step......Page 436 14.5.1.1. Random Forest......Page 437 14.5.1.2. Neural Network......Page 438 14.5.1.4. Naïve Bayesian Classification......Page 441 14.5.1.5. Multinomial Logistic Regression Model......Page 442 14.5.2. Model Selection......Page 444 14.5.3. Model Refinement......Page 447 14.6. Model Deployment......Page 449 14.7. Concluding Remarks......Page 453 References......Page 454 15.1. Introduction......Page 458 15.2. Data Extraction from PCAP to CSV File......Page 459 15.3. Data Importation from CSV File to R......Page 460 15.4. Dimension Reduction Via PCA......Page 461 15.5. Initial Data Exploration Via Graphs......Page 463 15.6. Variables Scaling and Samples Selection......Page 465 15.7. Clustering for Segmenting the FQDN......Page 466 15.8. Building Routing Table Thanks to Clustering......Page 469 15.9. Building Routing Table Thanks to Mixed Integer Linear Programming......Page 471 15.10. Building Routing Table Via a Heuristic......Page 474 15.11. Final Evaluation......Page 475 15.12. Conclusion......Page 477 References......Page 478 Index......Page 480

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. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.

R code, Data and color figures for the book are provided at the RDataMining.com website.

• Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries
• Presents various case studies in real-world applications, which will help readers to apply the techniques in their work
• Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

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. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries. Presents various case studies in real-world applications, which will help readers to apply the techniques in their work. Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves 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