DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data 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 Bibliography Index

Loading Data

The Data tab is the starting point for Rattle, and is where we load a specific dataset into Rattle.

Rattle is able to load data from various sources. Support is directly included for comma separated data files (.csv files as might be exported by a spreadsheet which use commas to separate variable values in a record--see Section 3.3), tab separated files (.txt, which are also commonly exported from spreadsheets and use the tab character to separate columns, rather than commas), a common data mining dataset format used by Weka (.arff files--see Section 3.4), and from an ODBC connection (thus allowing connection to an enormous collection of data sources including MS/Excel, MS/Access, SQL Server, Oracle, IBM DB2, Teradata, MySQL, Postgress, and SQLite--see Section 3.5).

When loading data into Rattle certain special strings are used to identify variable roles. For example, if the variable names starts with ID then the variable is marked as having an ID role See Section 3.10 for details.

Underneath Rattle, R is very flexible in where it obtains its data from, and data from almost any source can be loaded. Consequently, Rattle is able to access this same variety of sources. It does, however, require the loading of the data into the R console and then within Rattle loading it as an R Dataset. All kinds of additional data sources can be loaded directly into R--including loading data directly from SAS, SPSS, Minitab, Oracle, MySQL, and SQLite, as well as data formats including NCSA's HDF5 (Hierarchical Data Format^3.1) and UCAR's NETCDF (Network Common Data Form^3.2).

Once a dataset has been identified the name of the dataset will be displayed in the title of the Rattle window, as in Figure 3.1.

Figure 3.1: Rattle title bar showing the file name

The remainder of this Chapter covers the loading of data sources directly supported by Rattle.

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