A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
A new concise representation of frequent itemsets using generators and a positive border
Knowledge and Information Systems
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
Knowledge and Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Mining Regular Patterns in Transactional Databases
IEICE - Transactions on Information and Systems
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
Efficient mining of skyline objects in subspaces over data streams
Knowledge and Information Systems
Mining top-k frequent closed itemsets over data streams using the sliding window model
Expert Systems with Applications: An International Journal
Inclusion problems in trace monoids
Cybernetics and Systems Analysis
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Mining informative rule set for prediction over a sliding window
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Mining relaxed closed subspace clusters
Proceedings of the 48th Annual Southeast Regional Conference
ACM Transactions on Database Systems (TODS)
SPO-Tree: efficient single pass ordered incremental pattern mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
Efficient mining of frequent items coupled with weight and /or support over progressive databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Computers & Mathematics with Applications
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
On evaluating stream learning algorithms
Machine Learning
Mining frequent itemsets in a stream
Information Systems
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
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This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets will make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding window. The selected itemsets contain a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than representative algorithms for the sate-of-the-art approaches.