Designing an inductive data stream management system: the stream mill experience
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Data Mining and Knowledge Discovery
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Mining uncertain data for constrained frequent sets
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams
DS '09 Proceedings of the 12th International Conference on Discovery Science
Approximate Frequent Itemset Discovery from Data Stream
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Expert Systems with Applications: An International Journal
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Data Mining and Knowledge Discovery
The augmented itemset tree: a data structure for online maximum frequent pattern mining
DS'11 Proceedings of the 14th international conference on Discovery science
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
A false negative maximal frequent itemset mining algorithm over stream
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Rare pattern mining on data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
A sliding window based algorithm for frequent closed itemset mining over data streams
Journal of Systems and Software
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Sequential pattern mining from trajectory data
Proceedings of the 17th International Database Engineering & Applications Symposium
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining frequent itemsets in a stream
Information Systems
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response. In this paper, we introduce a novel verification algorithm which we then use to improve the performance of monitoring and mining tasks for association rules. Thus, we propose a frequent itemset mining method for sliding windows, which is faster than the state-of-the-art methods - in fact, its running time that is nearly constant with respect to the window size entails the mining of much larger windows than it was possible before. The performance of other frequent itemset mining methods (including those on static data) can be improved likewise, by replacing their counting methods (e.g., those using hash trees) by our verification algorithm.