DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining 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 Glossary Bibliography Index

Typographical Conventions

We use the R language to illustrate concepts and modelling in data mining. R is both a programming language and an interpreter. When we illustrate interactive sessions with R we will generally show the R prompt, which by default is ``>''. This allows the output from R to more easily be distinguished. However, we generally do not include the continuation prompt (a ``+'') which appears when a single command extends over multiple lines, as this makes it more difficult to cut-and-paste from the examples in the electronic version of the book.

In providing example output from commands, at times we will truncate the listing and indicate missing components with [...]. While most examples will illustrate the output exactly as it appears in R there will be times where the format will be modified slightly to fit with publication limitations. This might involve silently removing or adding blank lines.

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