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

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 IPD, RealKrimp, and Slim.

Automated summary

Research shows that there is great potential for development in the field of numerical dataset mining. The Mint algorithm proposed in this paper, based on the MDL principle, is able to discover useful, non-redundant, overlapping patterns that cover meaningful groups of objects.