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HYPE: Mining Hierarchical Sequential Patterns

Marc Plantevit 1 Anne Laurent 1 Maguelonne Teisseire 1 
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Mining data warehouses is still an open problem as few approaches really take into account the specifities of this framework (e.g. multidimensionnality, hierarchies, historized data). Multidimensional sequential patterns have been studied. However, they do not provide any way to handle hierarchies. In this paper, we propose an original method of extraction of sequential patterns taking into account the hierarchies. This method extracts more accurate knowledge and extends our preceding approach \m. We define the concepts related to our problems as well as the associated algorithms. The experiments which we carried out show the interest of our proposal.
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Submitted on : Tuesday, March 6, 2007 - 12:50:32 PM
Last modification on : Friday, August 5, 2022 - 10:46:38 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 11:14:07 PM


  • HAL Id : lirmm-00135019, version 1



Marc Plantevit, Anne Laurent, Maguelonne Teisseire. HYPE: Mining Hierarchical Sequential Patterns. DOLAP'06: ACM Ninth International Workshop on Data Warehousing and OLAP, Nov 2006, Arlington, VA, USA, pp.8. ⟨lirmm-00135019⟩



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