Towards a new approach for mining frequent itemsets on data stream

Chedy Raïssi 1 Pascal Poncelet 1 Maguelonne Teisseire 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. In this paper we propose a new approach for mining itemsets. Our approach has the following advantages: an efficient representation of items and a novel data structure to maintain frequent patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent itemsets over an arbitrary time interval. Furthermore our approach produces an approximate answer with an assurance that it will not bypass user-defined frequency and temporal thresholds. Finally the proposed method is analyzed by a series of experiments on different datasets.
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Article dans une revue
Journal of Intelligent Information Systems, Springer Verlag, 2007, 28 (1), pp.23-36. 〈http://dx.doi.org/10.1007/s10844-006-0002-3〉. 〈10.1007/s10844-006-0002-3〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00197166
Contributeur : Chedy Raïssi <>
Soumis le : vendredi 14 décembre 2007 - 14:00:56
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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Chedy Raïssi, Pascal Poncelet, Maguelonne Teisseire. Towards a new approach for mining frequent itemsets on data stream. Journal of Intelligent Information Systems, Springer Verlag, 2007, 28 (1), pp.23-36. 〈http://dx.doi.org/10.1007/s10844-006-0002-3〉. 〈10.1007/s10844-006-0002-3〉. 〈lirmm-00197166〉

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