Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Wavelets for computer graphics: theory and applications
Wavelets for computer graphics: theory and applications
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Association Rules for Expressing Gradual Dependencies
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Towards a new approach for mining frequent itemsets on data stream
Journal of Intelligent Information Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Web usage mining: extracting unexpected periods from web logs
Data Mining and Knowledge Discovery
Record linkage for database integration using fuzzy integrals
International Journal of Intelligent Systems
Mining frequent items in a stream using flexible windows
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Mining Frequent Itemsets in a Stream
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Fast extraction of gradual association rules: a heuristic based method
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Planning based on reasoning about information changes
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Temporal approach to association rule mining using t-tree and p-tree
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
OWA operators in data modeling and reidentification
IEEE Transactions on Fuzzy Systems
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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.