Data Mining Algorithms In R/Packages/RWeka/Weka filters

Description R interfaces to Weka filters. Usage Normalize(formula, data, subset, na.action, control = NULL) Discretize(formula, data, subset, na.action, control = NULL) Arguments formula, a symbolic description of a model. Note that for unsupervised filters the response can be omitted. data, an optional data frame containing the variables in the model. subset, an optional vector specifying a subset of observations to be used in the fitting process. na.action, a function which indicates what should happen when the data contain NAs. control, an object of class Weka_control, or a character vector of control options, or NULL (default). Details Normalize implements an unsupervised filter that normalizes all instances of a dataset to have a given norm. Only numeric values are considered, and the class attribute is ignored. Discretize implements a supervised instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani’s MDL method (the default). Note that these methods ignore nominal attributes, i.e., variables of class factor. Value A data frame Example w <- read.arff(system.file("arff","weather.arff", package = "RWeka")) m1 <- Normalize(~., data = w) m2 <- Discretize(play ~., data = w)