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

Histogram

A histogram is a useful graphical view of the spread of the data. A histogram plot in Rattle includes three components. The first of these is obviously the coloured vertical bars. The default behaviour of R is to partition the data into ranges, and the frequency of each range is display as the bar. This is a lot of text, just as a place holder for now. This is a lot of text, just as a place holder for now. This is a lot of text, just as a place holder for now. This is a lot of text, just as a place holder for now. This is a lot of text, just as a place holder for now.

library(rattle)
data(audit)
hs <- hist(audit$Income, main="", xlab="", col=rainbow(10))
dens <- density(audit$Income)
rs <- max(hs$counts)/max(dens$y)
lines(dens$x, dens$y*rs, type="l")
rug(audit$Income)
title(main="Distribution of Income",
      sub=paste("Rattle", Sys.time(), Sys.info()["user"]))

http://rattle.togaware.com/code/rattle-audit-explore-distr-hist-income.R

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