DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Rattle Data Miner Sourcing Data Selecting Data Exploring Data Transforming Data Building Classification Models Unsupervised Modelling Model Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models 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 Glossary Bibliography Index

Sourcing Data

Data is the starting point for all data mining--without it there is nothing to mine. Today there is certainly no shortage of data--but turning that data into information and knowledge is no simple matter. In this chapter we explore issues relating to data, in particular, loading and selecting the data for data mining.

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