  Pattern mining is well established in data mining research, especially for
mining binary datasets. Surprisingly, there is much less work about numerical
pattern mining and this research area remains under-explored. In this paper, we
propose Mint, an efficient MDL-based algorithm for mining numerical datasets.
The MDL principle is a robust and reliable framework widely used in pattern
mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering
useful patterns and returning a set of non-redundant overlapping patterns with
well-defined boundaries and covering meaningful groups of objects. Mint is not
alone in the category of numerical pattern miners based on MDL. In the
experiments presented in the paper we show that Mint outperforms competitors
among which Slim and RealKrimp.
