Binning is based on a specified number of bins. These methods are used as discretization methods for data reduction and concept hierarchy generation. For example, attribute values can be discretized by distributing the values into bin and replacing each bin by the mean bin value or bin median value. These techniques can be applied recursively to the resulting partitions to generate concept hierarchies. Binning is sensitive to the user-specified number of bins, as well as the presence of outliers.
To perform data discretization by binning we will use the discretize() function contained in the arule package. This function implements several basic unsupervized methods to convert continuous variables into a categorical ...
