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

TL;DR

Mint introduces an efficient MDL-based algorithm for mining meaningful, non-redundant numerical patterns, outperforming existing methods like Slim and RealKrimp in experimental evaluations.

Contribution

The paper presents Mint, a novel MDL-based approach specifically designed for numerical pattern mining, addressing a less-explored area in data mining.

Findings

Mint outperforms Slim and RealKrimp in experiments.

Mint discovers non-redundant, overlapping patterns with clear boundaries.

The approach effectively captures meaningful groups in numerical datasets.

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 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.