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

Loading Data

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

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Rattle is able to load data from various sources. Support is directly included in Rattle for comma separated data files (.csv files as might be exported by a spreadsheet), tab separated files (.txt, which are also commonly exported from spreadsheets) 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, and Postgress).

Underneath, 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. Refer to Chapter  for details on loading all kinds of data into R, including loading data directly from MS/Excel spreadsheets, MS/Access databases, SAS, SPSS, Minitab, Oracle, MySQL, and SQLite.

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