Mint：基于MDL的感兴趣数值模式集挖掘方法
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets
摘要：
模式挖掘是数据采掘研究中的一个重要领域，尤其是在二值数据集的挖掘中。令人惊讶的是，关于数字模式挖掘的工作要少得多，而且这一研究领域仍然没有得到充分的探索。在本文中，我们提出了Mint，一个有效的基于MDL的算法挖掘数值数据集。MDL原则是一个健壮可靠的框架，广泛应用于模式挖掘和子组发现。在Mint中，我们重用MDL来发现有用的模式，并返回一组非冗余的重叠模式，这些模式具有定义良好的边界并覆盖有意义的对象组。Mint并不是唯一一个基于MDL的数字模式挖掘器。在本文中提出的实验中，我们表明，薄荷优于竞争对手，其中苗条和RealKrimp。
英文摘要：
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.
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