In multidimensional database mining, constrained multidimensional patterns differ from the well-known frequent patterns from both conceptual and logical points of view because of a common structure and the ability to support various types of constraints. Classical data mining techniques are based on the power set lattice of binary attribute values and, even adapted, are not suitable when addressing the discovery of constrained multidimensional patterns. In this paper, the authors propose a foundation for various multidimensional database mining problems by introducing a new algebraic structure called cube lattice, which characterizes the search space to be explored. This paper takes into consideration monotone and/or anti-monotone constraints enforced when mining multidimensional patterns. The authors propose condensed representations of the constrained cube lattice, which is a convex space, and present a generalized levelwise algorithm for computing them. Additionally, the authors consider the formalization of existing data cubes, and the discovery of frequent multidimensional patterns, while introducing a perfect concise representation from which any solution provided with its conjunction, disjunction and negation frequencies. Finally, emphasis on advantages of the cube lattice when compared to the power set lattice of binary attributes in multidimensional database mining are placed.
Mining Frequent Closed Ordered Subtrees under Anti-monotone Constraints by using Restricted Occurrence Matching
