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

Removing Outliers

Tests for outliers have primarily been superseded by the use of robust methods. Outlier tests are poor in that outliers tend to damage results long before they are detected. Robust methods attempt to compensate rather than reject outliers. RandomForrest modelling helps avoid the issue of outliers.

You can get a list of what the boxplot function thinks are outliers:

> load("wine.RData")
> bp <- boxplot(wine$Ash, plot=FALSE)
> bp$out
[1] 3.22 1.36 3.23

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