Beschreibung:
<jats:title>Abstract</jats:title><jats:p>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<jats:sc>Mint</jats:sc>, 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<jats:sc>Mint</jats:sc>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.<jats:sc>Mint</jats:sc>is not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show that<jats:sc>Mint</jats:sc>outperforms competitors among which IPD,<jats:sc>RealKrimp</jats:sc>, and<jats:sc>Slim</jats:sc>.</jats:p>