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

Obtaining Data

Data can be sourced from many diverse repositories. Data is often stored today in relational databases like Oracle, MySQL, and SQLite, and accessed by the structured query language (SQL). It could be stored in systems like SAS and many other alternative proprietary systems. Many such systems provide access to the data in a number of forms and have particularly well tuned and efficient procedures for accessing the data. Such access is fine for many applications, but the user tends to be limited to the functionality provided by the particular application environment.

R provides a wealth of tools for importing data. In this section we overview the functions used to access common data formats. For a more complete exposition see 05:r_data_impor_expor.

Data warehouses

OLTP systems such as billing systems

Special databases, such as marketing and sales

Excel, Access

Flat files

External data sources, e.g., demographic data, market survey data

Describe output variable and input variables.

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