Cube Lattices: A Framework for Multidimensional Data Mining
Cube Lattices: A Framework for Multidimensional Data Mining
复制标题
立方体格子：多维数据挖掘的框架
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
L. Lakhal
中科院分区：
文献类型：
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作者：
Alain Casali;R. Cicchetti;L. Lakhal
从概念和逻辑的角度来看，受约束的多维模式与众所周知的频繁模式不同，因为它们具有通用的结构并支持各种类型的约束。经典的数据挖掘技术是基于二进制属性的幂集格，甚至扩展，不适合解决约束多维模式的发现。在本文中，我们提出了各种多维数据挖掘问题的基础，通过引入一个新的代数结构称为立方体格的特点，搜索空间进行探索。我们考虑到挖掘多维模式时执行的单调和/或反单调约束。此外，我们提出了约束立方体格，这是一个凸空间的压缩表示。最后，我们把重点放在优势的立方体格相比，用于多维数据挖掘的二进制属性的幂集格。
Constrained multidimensional patterns differ from the well-known frequent patterns from a conceptual and logical points of view because they are provided with a common structure and support various types of constraints. Classical data mining techniques are based on the power set lattice of binary attributes and, even extended, are not suitable when addressing the discovery of constrained multidimensional patterns. In this paper we propose a foundation for various multidimensional data mining problems by introducing a new algebraic structure called cube lattice which characterizes the search space to be explored. We take into consideration monotone and/or antimonotone constraints enforced when mining multidimensional patterns. In addition, we propose condensed representations of the constrained cube lattice which is a convex space. Finally, we place emphasis on advantages of the cube lattice when compared to the power set lattice of binary attributes used for multidimensional data mining.