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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 3/2010

01.12.2010

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

verfasst von: Jordi Nin, Anne Laurent, Pascal Poncelet

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2010

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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.
Vorheriger Artikel Semantic-distance based evaluation of ranking queries over relational databases
Nächster Artikel A performance evaluation framework for association mining in spatial data

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Fußnoten
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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Metadaten
Titel
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
verfasst von
Jordi Nin
Anne Laurent
Pascal Poncelet
Publikationsdatum
01.12.2010
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 3/2010
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-009-0112-9

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