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Extracting semantics from data cubes using cube transversals and closures
2003
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
In this paper we propose a lattice-based approach intended for extracting semantics from datacubes: borders of version spaces for supervised classification, closed cube lattice to summarize the semantics of datacubes w.r.t. COUNT, SUM, and covering graph of the quotient cube as a visualization tool of minimal multidimensional associations. With this intention, we introduce two novel concepts: the cube transversals and the cube closures over the cube lattice of a categorical database relation.
doi:10.1145/956755.956762
fatcat:4apxirmavnbwzacfpmx4pqkdwq