Data Mining Applications with RData 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 |
Contents
| 1 | |
| 35 | |
Discovery of Emergent Issues and Controversies in Anthropology Using Text Mining Topic Modeling and Social Ne | 63 |
Text Mining and Network Analysis of Digital Libraries in R | 95 |
Recommender Systems in R | 117 |
Response Modeling in Direct Marketing A Data MiningBased Approach for Target Selection | 153 |
Caravan Insurance Customer Profile Modeling with R | 181 |
Selecting Best Features for Predicting Bank Loan Default | 229 |
A RealTime Property Value Index Based on Web Data | 273 |
Predicting Seabed Hardness Using Random Forest in R | 299 |
Supervised Classification of Images Applied to Plankton Samples Using R and Zooimage | 331 |
Crime Analyses Using R | 367 |
Football Mining with R | 397 |
Analyzing Internet DNSSEC Traffic with R for Resolving Platform Optimization | 435 |
| 457 | |
A Choquet Integral Toolbox and Its Application in Customer Preference Analysis | 247 |
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Common terms and phrases
accuracy algorithms analysis applied backscatter Breiman Calanoida calculated chapter Choquet Integral classifier cluster computed confusion matrix Cont corpus covariates crime criteria cross-validation data frame data mining data.frame dataset direct marketing distribution document-term matrix documents error estimated evaluation example F-score factors Figure FQDN frequency function fuzzy measure graph Hadoop identify implementation important indicates input Interaction Index iterations key/value pairs load machine learning MapReduce messages method MLogit NNET object optimal output package version parameters performance plankton plot policies predictive model predictors prock random forest regression response model retweeted Rfmtool RHIPE ROC curve routing table sample seabed hardness Section server Shapley value spatial statistics step subset support vector machine SVM model target techniques test set text mining token topic training set Twitter users validation visualization zooimage zooplankton