Reduced representations of Emerging Cubes for OLAP database mining

Sebastien Nedjar (1) , Alain Casali (1) , Rosine Cicchetti (1) , Lotfi Lakhal (1)

1 LIF - Laboratoire d'informatique Fondamentale de Marseille - UMR 6166

In this paper, we investigate reduced representations for the Emerging Cube. We use the borders, classical in data mining, for the Emerging Cube. These borders can support classification tasks to know whether a trend is emerging or not. However, the borders do not make possible to retrieve the measure values. This is why we introduce two new and reduced representations without measure loss: the L-Emerging Closed Cube and Emerging Quotient Cube. We state the relationship between the introduced representations. Experiments performed on various data sets are intended to measure the size of the three reduced representations.

Mots clés: data mining; OLAP mining; data warehouse; data cube; trend analysis; emerging cube; reduced representation; cube closure; online analytical processing; data mining.

Domaines: Base de données [cs.DB]

Sebastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal. Reduced representations of Emerging Cubes for OLAP database mining. International Journal of Business Intelligence and Data Mining, 2009, 4 (3/4), pp.267 - 300. ⟨10.1504/IJBIDM.2009.029075⟩. ⟨hal-00464138⟩