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Pattern mining is well established in data mining research, especially formining binary datasets. Surprisingly, there is much less work about numericalpattern mining and this research area remains under-explored. In this paper, wepropose Mint, an efficient MDL-based algorithm for mining numerical datasets.The MDL principle is a robust and reliable framework widely used in patternmining, and as well in subgroup discovery. In Mint we reuse MDL for discoveringuseful patterns and returning a set of non-redundant overlapping patterns withwell-defined boundaries and covering meaningful groups of objects. Mint is notalone in the category of numerical pattern miners based on MDL. In theexperiments presented in the paper we show that Mint outperforms competitorsamong which Slim and RealKrimp.

Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets