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On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies

  • Eya Ben AhmedEmail author
  • Ahlem Nabli
  • Faïez Gargouri
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 17)

Abstract

The decision-making process can be supported by many pioneering technologies such as Data Warehouse (DW), On-Line Analytical Processing (OLAP), and Data Mining (DM). Much research found in literature is aimed at integrating these popular research topics. In this chapter, we focus on discovering cyclic patterns from advanced multi-dimensional context, specially parallel hierarchies where more than one hierarchy is associated to given dimension in respect to several analytical purposes. Thus, we introduce a new framework for cyclic association rules mining from multiple hierarchies. To exemplify our proposal, an illustrative example is provided throughout the article. Finally, we perform intensive experiments on synthetic and real data to emphasize the interest of our approach.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Higher Institute of Management of TunisUniversity of TunisTunisTunisia
  2. 2.Faculty of Sciences of SfaxSfax UniversitySfaxTunisia
  3. 3.Higher Institute of Computer Science and Multimedia of SfaxSfax UniversitySfaxTunisia

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