Abstract
It is well recognized that data cubes often produce huge outputs. Several efforts were devoted to this problem through closed cubes, where cells preserving aggregation semantics are losslessly reduced to one cell. In this paper, we introduce the concept of closed non derivable data cube, denoted \(\mathcal{CND}\) - \(\mathcal{C}\)ube, which generalizes the notion of bi-dimensional frequent closed non derivable patterns to the multidimensional context. We propose a novel algorithm to mine \(\mathcal{CND}\) - \(\mathcal{C}\)ube from multidimensional databases considering three anti-monotone constraints, namely “to be frequent”, “to be non derivable” and “to be minimal generator”. Experiments show that our proposal provides the smallest representation of a data cube and thus is the most efficient for saving storage space.
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Brahmi, H., Hamrouni, T., Ben Messaoud, R., Ben Yahia, S. (2009). Closed Non Derivable Data Cubes Based on Non Derivable Minimal Generators. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_9
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DOI: https://doi.org/10.1007/978-3-642-03348-3_9
Publisher Name: Springer, Berlin, Heidelberg
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