Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets
Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli · Data Mining and Knowledge Discovery · 2021
Abstract 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 proposeMint, 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. InMintwe 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.Mintis not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show thatMintoutperforms competitors among which IPD,RealKrimp, andSlim.