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

Project Menu and Buttons

A project is a packaging of a dataset, variable selections, explorations, clusters and models built from the data. Rattle allows projects to be saved for later resumption of the work or for sharing the data mining project with other users.

A project is typically saved to a file with the .rattle extension (although in reality it is just a standard .RData file.

At a later time you can load a project into rattle to restore the data, models, and other displayed information relating to the project, and resume your data mining from that point. You can also share these project files with other Rattle users, which is quite useful for data mining teams.

You can rename the files, keeping the .rattle extension, without impacting the project file itself--that is, the file name has no formal bearing on the contents, so use it to be descriptive--but best to avoid vacant spaces and unusual characters!

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