Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Mining frequent closed itemsets from a landmark window over online data streams
Computers & Mathematics with Applications
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams
IEEE Transactions on Knowledge and Data Engineering
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
EclatDS: An efficient sliding window based frequent pattern mining method for data streams
Intelligent Data Analysis
An efficient algorithm for frequent itemset mining on data streams
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
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Frequent pattern mining over data streams is an important problem in the context of data mining and knowledge discovery. Mining frequent closed itemsets within sliding window instead of complete set of frequent itemset is very interesting since it needs a limited amount of memory and processing power. Moreover, handling concept change within a compact set of closed patterns is faster. However, it requires flexible and efficient data structures as well as intuitive algorithms. In this paper, we have introduced an effective and efficient algorithm for closed frequent itemset mining over data streams operating in the sliding window model. This algorithm uses a novel data structure for storing transactions of the window and corresponding frequent closed itemsets. Moreover, the support of a new frequent closed itemset is efficiently computed and an old pattern is removed from the monitoring set when it is no longer frequent closed itemset. Extensive experiments on both real and synthetic data streams show that the proposed algorithm is superior to previously devised algorithms in terms of runtime and memory usage.