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

Introduction

Rattle (the R Analytical Tool To Learn Easily) is a graphical data mining application built upon the statistical language #6717#>R#6718#>Concepts!Rhttp://en.wikipedia.org/wiki/R_programming_languageR. An understanding of R is not required in order to use Rattle. However, a basic introduction is provided in Chapter . Rattle is simple to use, quick to deploy, and allows us to rapidly work through the modelling phase of a data mining project. R, on the other hand, provides a very powerful language for performing data mining, and when we need to fine tune our data mining projects we can always migrate from Rattle to R simply by taking Rattle's underlying commands and deploying them within the R console.

Rattle uses the Gnome graphical user interface and runs under various operating systems, including GNU/Linux, Macintosh OS/X, and MS/Windows. Its intuitive user interface takes us through the basic steps of data mining, as well as illustrating (through a log textview) the actual R code that is used to achieve this. The R code itself can actually be saved to file and used as an automatic script which can be loaded into R (outside of Rattle) to repeat any data mining exercise.

While Rattle by itself may be sufficient for all of a user's needs, it also provides a stepping stone to more sophisticated processing and modelling in R itself. The user is not limited to my views and ideas about how things should be done. For sophisticated and unconstrained data mining, the experienced user can progress to interacting directly with a powerful language.

In this chapter we present the Rattle interface, and its basic environment for interaction, including menus and toolbars, and saving and loading projects. Chapter  works through the process of loading data into Rattle and Chapter  presents the various options within Rattle for exploring our data. We then go through the process of building models and evaluating the models in Chapters  to .

We start with the installation of Rattle.

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