This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
iris is a data frame with 150 cases (rows) and 5 variables (columns) named:
A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml/.
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2):179-188, 1936.
## Not run: devAskNewPage(ask = TRUE) data(iris) # Show level attributes. levels(iris[["Class"]]) # Split dataset into train (75 set.seed(5) Iris <- split(p = 0.6, Dataset = iris, class = 5) # Estimate number of components, component weights and component # parameters for train subsets. n <- range(a.ntrain(Iris)) irisest <- REBMIX(model = "REBMVNORM", Dataset = a.train(Iris), Preprocessing = "histogram", cmax = 10, Criterion = "ICL-BIC", EMcontrol = new("EM.Control", strategy = "single")) plot(irisest, pos = 1, nrow = 3, ncol = 2, what = c("pdf")) plot(irisest, pos = 2, nrow = 3, ncol = 2, what = c("pdf")) plot(irisest, pos = 3, nrow = 3, ncol = 2, what = c("pdf")) # Selected chunks. iriscla <- RCLSMIX(model = "RCLSMVNORM", x = list(irisest), Dataset = a.test(Iris), Zt = a.Zt(Iris)) iriscla summary(iriscla) # Plot selected chunks. plot(iriscla, nrow = 3, ncol = 2) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.