R and Data Mining: Examples and Case StudiesR and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
Contents
| 1 | |
1 | 27 |
Regression | 41 |
Clustering | 51 |
Outlier Detection | 63 |
5 | 73 |
6 | 87 |
Text Mining | 105 |
Analysis and Forecasting of House Price | 137 |
Customer Response Prediction and Profit | 151 |
Predictive Modeling of Big Data with Limited | 181 |
1 | 213 |
R Reference Card for Data Mining | 221 |
13 | 226 |
| 229 | |
Social Network Analysis | 123 |
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Common terms and phrases
2013 Yanchang Zhao Age=Adult algorithm association rule mining average barplot big data boxplot build CARDPM12 categorical variables chart Class=2nd Class=3rd Class=Crew classification components ctree data frame Data Mining data.frame database dataset decision trees describe cup98 Discrete Wavelet Transform donation amount Dynamic Time Warping Elsevier Inc examples Factor foreach forecasting frequent function GENDER graph graph mining hierarchical clustering interface iris2 itemsets k-medoids labels LASTDATE levels MaxDepth MaxSurrogate MINRAMNT MinSplit missing values myCorpus myCtree NGIFTALL Node number of clusters outlier detection package version parallel computing parallel coordinates parameters PEPSTRFL Percentage Percentile predict Published by Elsevier random forest Reference Card regression result RFA_2A rule2 scatter plot score data Sepal.Length Sepal.Width Series Analysis setosa Sex=Female Sex=Male social network analysis statistic stats Survived=Yes TARGET_B term-document matrix Text Mining tm_map topic models training data tutorial tweets Twitter versicolor vertices virginica wavelet words x$splitpoint