AbstractCubegrades are a generalization of association rules which represent how a set of measures (aggregates) is affected by modifying a cube through specialization (roll down), 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 use arbitrary aggregate measures, not just COUNT, as association rules do. Cubegrades are atoms which can support sophisticated “what if” analysis tasks dealing with the 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. We formally define cubegrades, show methods to generate them by using efficient pruning algorithms and finally define two query languages to generate and retrieve sets of cubegrades which satisfy user-defined conditions. We also demonstrate how to evaluate simple cubegrade queries and conclude with a number of open questions and possible extensions of the work.
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