In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation. It is a form of discretization in general and also of binning, as in making a histogram. Whenever continuous data is discretized, there is always some amount of discretization error. The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand.
Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies).
Many machine learning algorithms are known to produce better models by discretizing continuous attributes.
This is a partial list of software that implement MDL algorithm.