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Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach

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Abstract

Gradual rules allow users to be provided with rules describing the ordering correlations among attributes. Such a rule is for instance given by the higher the salary and the lower the number of cars, the higher the number of tourist travels. Previously intensively used in fuzzy command systems, these rules were manually provided to the system. More recently, they have received attention from the data mining community and methods have been defined to automatically extract and maintain gradual rules from numerical databases. However, no method has been shown to be able to handle data streams, as no method is scalable enough to manage the high rate which stream data arrive at. In this paper, we thus propose an original approach to mine data streams for gradual rules. Our method is based on B-Trees and OWA (Ordered Weighted Aggregation) operator in order to speed up the process. B-Trees are used to store already-known gradual rules in order to maintain the knowledge over time, while OWA operators provide a fast way to discard non relevant data.

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Notes

  1. If a tuple has an attribute lower or higher than the minimum or maximum value predefined, such value can be normalized as 0 (for the minimum) or 1 (for the maximum).

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Acknowledgements

Partial support by the European Community through the 7th Framework Programme Marie Curie Intra-European fellowship, contract No 235226 is acknowledged. Partial support by the Spanish MEC and Generalitat de Catalunya (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004, eAEGIS – TSI2007-65406-C03-02 and 2009-SGR-7) is also acknowledged This work was also done in the context of the French ANR Project MIDAS (AND-07-MDCO-008).

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Authors and Affiliations

  1. LAAS, Lab. d’Analyse et d’Architecture des Systèmes, CNRS, Centre National de la Recherche Scientifique, 7, Avenue du Colonel Roche, Toulouse, 31077, France

    Jordi Nin

  2. LIRMM - CNRS UMR 5506, Univ. Montpellier 2, 161 rue Ada, 34395, Montpellier Cedex 5, France

    Jordi Nin, Anne Laurent & Pascal Poncelet

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  1. Jordi Nin
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  2. Anne Laurent
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  3. Pascal Poncelet
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Correspondence to Jordi Nin.

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Nin, J., Laurent, A. & Poncelet, P. Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach. J Intell Inf Syst 35, 447–463 (2010). https://doi.org/10.1007/s10844-009-0112-9

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  • DOI: https://doi.org/10.1007/s10844-009-0112-9

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