Title: Reduced representations of Emerging Cubes for OLAP database mining

Authors: Sebastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal

Addresses: Laboratoire d'Informatique Fondamentale de Marseille (LIF), Aix-Marseille Universite – CNRS, Case 901, 163 Avenue de Luminy, 13288 Marseille Cedex 9, France. ' Laboratoire d'Informatique Fondamentale de Marseille (LIF), Aix-Marseille Universite – CNRS, Case 901, 163 Avenue de Luminy, 13288 Marseille Cedex 9, France. ' Laboratoire d'Informatique Fondamentale de Marseille (LIF), Aix-Marseille Universite – CNRS, Case 901, 163 Avenue de Luminy, 13288 Marseille Cedex 9, France. ' Laboratoire d'Informatique Fondamentale de Marseille (LIF), Aix-Marseille Universite – CNRS, Case 901, 163 Avenue de Luminy, 13288 Marseille Cedex 9, France

Abstract: 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.

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

DOI: 10.1504/IJBIDM.2009.029075

International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.3/4, pp.267 - 300

Published online: 03 Nov 2009 *

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