DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Introduction Getting Started Interacting with Rattle Data Exploring Data Test Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle Glossary Solutions to Exercises Bibliography Index Introduction Data Mining Types of Analysis Data Mining Applications A Framework for Modelling Agile Data Mining R Why R and Rattle? Data Preparation Number of Algorithms Repeatability Performance Pros and Cons Books on R Getting Started Interacting with R Loading Our First Data Set Building Our First Model Exploring the Data Where to Now? R Documentation Chapter Exercises Interacting with Rattle What is Rattle? The Initial Interface Interacting with Rattle Menus and Buttons Project Menu and Buttons Edit Menu Tools Menu and Toolbar Execute Export Settings Help Interacting with Plots Summary Data Nomenclature Loading Data into Rattle CSV Data Datasets Play Golf Weather Data Other Data Sources ARFF Data ODBC Sourced Data R Data R Dataset Data Entry Data Tab Options in Rattle Sampling Data Variable Roles Automatic Role Identification Weights Calculator Exploring Data Summarising Data Summary Describe Basics Kurtosis Skewness Missing Exploring Distributions Box Plot Histogram Cumulative Distribution Plot Benford's Law Other Digits Stratified Benford Plots Bar Plot Dot Plot Mosaic Plot Scatterplot Data Viewer Brushing Identify Multivariate Outliers Other Options Quality Plots Using R Further GGobi Documentation Correlation Analysis Hierarchical Correlation Principal Components Single Variable Overviews Test Transforming Data Rescale Data Recenter Scale [0,1] Rank Median/MAD Nolan Groups Impute Zero/Missing Mean/Median/Mode Constant Remap Binning Indicator Variables Join Categorics Math Transforms Outliers Cleanup Delete Ignored Delete Selected Delete Missing Delete Entities with Missing Cluster Analysis KMeans Export KMeans Clusters Discriminant Coordinates Plot Number of Clusters Hierarchical Clusters Association Analysis Basket Analysis General Rules Classification and Regression Models Building Models Risk Charts Regression Linear Regression Formalities Tutorial Example Tuning parameters Generalized Regression Formalities Tutorial Example Tuning parameters Logistic Regression Formalities Tutorial Example Tuning parameters Discussion Probit Regression Formalities Tutorial Example Tuning Parameters Multinomial Regression Formalities Tutorial Example Tuning Parameters Decision Trees Formalities Tutorial Example Tuning Parameters Priors (prior) Loss Matrix Min Split (Rarg[]minsplit) Min Bucket (minbucket) Complexity (cp) Max Depth (maxdepth) Other Options Boosting Formalities Tutorial Example Tuning Parameters Random Forests Formalities Tutorial Example Tuning Parameters Number of Trees Sample Size Number of Variables Neural Network Formalities Tutorial Example Tuning parameters Support Vector Machine Formalities Tutorial Example Tuning parameters Bibliographic Notes Evaluation The Evaluate Tab Confusion Matrix Measures Graphical Measures Cost Curves Lift ROC Curves Area Under Curve Precision versus Recall Sensitivity versus Specificity Predicted versus Observed Scoring Issues Model Selection Overfitting Imbalanced Classification Sampling Cost Based Learning Model Deployment and Interoperability SQL PMML XML for Data Bibliographic Notes Moving into R Interacting with R Basic Command Line Windows, Icons, Mouse, Pointer--WIMP The Current Rattle State Samples Projects The Rattle Log Further Tuning Models Emacs and ESS Troubleshooting Cairo A factor has new levels

Obtaining and Installing R

CRAN is the Comprehensive R Archive Network where we find the R installation package and all verified R packages. Installation of R is straightforward from a pre-compiled binary package that can be downloaded from there. The full details covering the installation of R for alternative environments is covered in Appendix VI.

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