Cubegrades: Generalizing association rules

Cubegrades are a generalization of association rules which represent how a set of measures (aggregates) is affected by modifying a cube through specialization (rolldown), generalization (rollup) and mutation (which is a change in one of the cube's dimensions). Cubegrades are significantly more expressive than association rules in capturing trends and patterns in data because they can use other standard aggregate measures, in addition to COUNT. Cubegrades are atoms which can support sophisticated "what if" analysis tasks dealing with behavior of arbitrary aggregates over different database segments. As such, cubegrades can be useful in marketing, sales analysis, and other typical data mining applications in business. In this paper we introduce the concept of cubegrades. We define them and give examples of their usage. We then describe in detail an important task for computing cubegrades: generation of significant cubes which is analogous to generating frequent sets. A novel Grid Based Pruning (GBP) method is employed for this purpose. We experimentally demonstrate the practicality of the method. We conclude with a number of open questions and possible extensions of the work.