DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

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: sources of data; storing data; and manipulating data. Example R code illustrates how data can be used for generating and extracting data from various data sources, and manipulating that data.

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