Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets
This work addresses the under-explored area of numerical pattern mining, providing an improved method for data miners to discover meaningful patterns in numerical datasets.
This paper introduces Mint, an efficient MDL-based algorithm designed for mining numerical datasets. Mint successfully identifies useful, non-redundant, and overlapping numerical patterns with clear boundaries, outperforming existing competitors like Slim and RealKrimp in experimental evaluations.
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.