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

Replace Indices By Names

> city.name <- c("Munich", "Paris", "Tokyo", "London", "Boston")
> X <- cbind(c(2, 5, 5), c(4, 1, 3))
> X
       [,1] [,2]
  [1,]    2    4
  [2,]    5    1
  [3,]    5    3
> matrix(city.name[X], ncol = 2)
       [,1]     [,2]    
  [1,] "Paris"  "London"
  [2,] "Boston" "Munich"
  [3,] "Boston" "Tokyo"

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.