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

Mean/Median

Often a simple, if not always satisfactory, choice for missing values is to use some ``central'' value of the variable. This is often the mean or median. We might choose to use the mean, for example, if the variable is otherwise generally normally distributed (and in particular does not have any skewness). If the data does exhibit some skewness though (e.g., there are a small number of very large values) then the median might be a better choice.

This is achieved in R with

crs$dataset[is.na(crs$dataset$Age), "Age"] <- mean(crs$dataset$Age, na.rm=T)
crs$dataset[is.na(crs$dataset$Age), "Age"] <- median(crs$dataset$Age, na.rm=T)

Whilst this is a simple and computationally quick approach, it is a very blunt approach to imputation and can lead to poor performance from the resulting models.

Refer to http://www.liacc.up.pt/ ltorgo/DataMiningWithR/PDF/DataMiningWithR.pngData Mining With R, from page 42, for more details.

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