Mining Multi-Dimensional and Multi-Level Sequential Patterns

Abstract : Multi-dimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. Compared to transactional data, this framework is par- ticular as the datasets contain huge volumes of historized and aggregated data defined over a set of dimensions, which can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data, in order to discover regular specific patterns. In this paper, we present a method for mining sequential patterns from multi-dimensional databases, taking at the same time advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the interest of this approach.
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Article dans une revue
ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2010, 4, pp.1-37
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00617320
Contributeur : Anne Laurent <>
Soumis le : vendredi 26 août 2011 - 22:25:19
Dernière modification le : vendredi 19 octobre 2018 - 01:14:15

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  • HAL Id : lirmm-00617320, version 1

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Marc Plantevit, Anne Laurent, Dominique Laurent, Maguelonne Teisseire, Yeow Wei Choong. Mining Multi-Dimensional and Multi-Level Sequential Patterns. ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2010, 4, pp.1-37. 〈lirmm-00617320〉

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