ISBN: 9780124115200

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, … Mehr…

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 Academic Press Databases Accuracy measure; Accuracy-v-Cut-off.; Anthropology; AUC; Bank Loan; Basel Committee; Bayesian inference; Betweenness centrality; choquet integral; Classification; Classification method; Classifier; Clustering; Collaborative filtering; Corporate exposure; Credit risk; Crime; Cross-validation; customer preferences; Customer Profile; Data cleaning; data mining; Data mining; Dimension reduction; Direct marketing; DNS; DNSSEC; Ecology; Event detection; Feature learning; Feature selection; Football data; fuzzy measure; Geographically weighted regression; ggplot2; GLMM; Hadoop; Housing prices; Image analysis; Insurance; interaction index; Internet; K-means; Kriging; Large data; Latent Dirichlet allocation; Load balancing; Low-rank matrix; Machine learning; Machine learning algorithms; Marine environment; Matrix factorization; Mixed model; Model evaluation; Negative binomial; Oceanography; Pea; Plankton; Power grid; Prediction; Predictive model; Predictive model building; Price indices; Principal component analysis; Probability of default; R; Recall-precision; Recommendation engine; Recommendation system; Regression; Response model; RHIPE; Semiparametric models; Sentiment analysis; shapley; Singular value decomposition; Smoothing; Social network analysis; Social networking analysis; Spatial analysis; Spatial prediction; Stochastic gradient descent; Support vector machine; Target marketing; Target selection; tcltk; Telecommunications; Text classification; Text mining; Topic modeling; Twitter; Variable selection; Visual analytics 9780124115118 DE,GB,ES,IT,FR English Computer Science, Elsevier Science<

ISBN: 9780124115200

Academic Press Programming Languages Accuracy measure; Accuracy-v-Cut-off.; Anthropology; AUC; Bank Loan; Basel Committee; Bayesian inference; Betweenness centrality; choquet integral; Cl… Mehr…

Academic Press Programming Languages Accuracy measure; Accuracy-v-Cut-off.; Anthropology; AUC; Bank Loan; Basel Committee; Bayesian inference; Betweenness centrality; choquet integral; Classification; Classification method; Classifier; Clustering; Collaborative filtering; Corporate exposure; Credit risk; Crime; Cross-validation; customer preferences; Customer Profile; Data cleaning; data mining; Data mining; Dimension reduction; Direct marketing; DNS; DNSSEC; Ecology; Event detection; Feature learning; Feature selection; Football data; fuzzy measure; Geographically weighted regression; ggplot2; GLMM; Hadoop; Housing prices; Image analysis; Insurance; interaction index; Internet; K-means; Kriging; Large data; Latent Dirichlet allocation; Load balancing; Low-rank matrix; Machine learning; Machine learning algorithms; Marine environment; Matrix factorization; Mixed model; Model evaluation; Negative binomial; Oceanography; Pea; Plankton; Power grid; Prediction; Predictive model; Predictive model building; Price indices; Principal component analysis; Probability of default; R; Recall-precision; Recommendation engine; Recommendation system; Regression; Response model; RHIPE; Semiparametric models; Sentiment analysis; shapley; Singular value decomposition; Smoothing; Social network analysis; Social networking analysis; Spatial analysis; Spatial prediction; Stochastic gradient descent; Support vector machine; Target marketing; Target selection; tcltk; Telecommunications; Text classification; Text mining; Topic modeling; Twitter; Variable selection; Visual analytics ,9780124115118 UK,GB,DE,ES,FR,IT,US,CA,MX,AU,NZ 20131126 English Computer Science, Elsevier Science<

ISBN: 9780124115200

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, … Mehr…

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 Digital Content>E-books>Technology>Apple>Other Database Tools, Elsevier Science Digital >16<

ISBN: 9780124115200

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, … Mehr…

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., Elsevier Science<

2013, ISBN: 9780124115200

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