A native extension of SQL for mining data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Research issues in data stream association rule mining
ACM SIGMOD Record
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining evolving data streams for frequent patterns
Pattern Recognition
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Towards a new approach for mining frequent itemsets on data stream
Journal of Intelligent Information Systems
Mining maximal frequent itemsets from data streams
Journal of Information Science
An on-line interactive method for finding association rules data streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Discovering during-temporal patterns (DTPs) in large temporal databases
Expert Systems with Applications: An International Journal
Incremental maintenance of generalized association rules under taxonomy evolution
Journal of Information Science
On-line generation association rules over data streams
Information and Software Technology
An efficient algorithm for mining frequent closed itemsets in dynamic transaction databases
International Journal of Intelligent Systems Technologies and Applications
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Interactive mining of frequent itemsets over arbitrary time intervals in a data stream
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
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 top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Short communication: TOPSIS: Finding Top-K significant N-itemsets in sliding windows adaptively
Knowledge-Based Systems
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficient Mining of Frequent Itemsets from Data Streams
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Negative Generator Border for Effective Pattern Maintenance
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Moment+: Mining Closed Frequent Itemsets over Data Stream
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Mining Multidimensional Sequential Patterns over Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
RETRACTED: Efficient mining of temporal emerging itemsets from data streams
Expert Systems with Applications: An International Journal
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Pattern discovery and change detection of online music query streams
Multimedia Tools and Applications
Incrementally Mining Recently Repeating Patterns over Data Streams
New Frontiers in Applied Data Mining
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
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
On pushing weight constraints deeply into frequent itemset mining
Intelligent Data Analysis
Data Mining and Knowledge Discovery
Optimal sampling from sliding windows
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
WSFI-Mine: Mining Frequent Patterns in Data Streams
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Online Evaluation of Patterns from Evolving Web Data Streams
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining frequent itemsets in time-varying data streams
Proceedings of the 18th ACM conference on Information and knowledge management
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
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Online mining of temporal maximal utility itemsets from data streams
Proceedings of the 2010 ACM Symposium on Applied Computing
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
CLAIM: an efficient method for relaxed frequent closed itemsets mining over stream data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Discovery of frequent distributed event patterns in sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
A test paradigm for detecting changes in transactional data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Mining top-k frequent closed itemsets over data streams using the sliding window model
Expert Systems with Applications: An International Journal
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
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
Robust ensemble learning for mining noisy data streams
Decision Support Systems
On dense pattern mining in graph streams
Proceedings of the VLDB Endowment
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Effective Computations on Sliding Windows
SIAM Journal on Computing
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Expert Systems with Applications: An International Journal
Automatic assignment of item weights for pattern mining on data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Classification rule mining for a stream of perennial objects
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
SPO-Tree: efficient single pass ordered incremental pattern mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
The augmented itemset tree: a data structure for online maximum frequent pattern mining
DS'11 Proceedings of the 14th international conference on Discovery science
Search method of time sensitive frequent itemsets in data streams
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Variable support mining of frequent itemsets over data streams using synopsis vectors
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An incremental algorithm for mining generators representation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A sliding window-based false-negative approach for ubiquitous data stream analysis
International Journal of Communication Systems
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Rare pattern mining on data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
ACM Transactions on Management Information Systems (TMIS)
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Stream mining on univariate uncertain data
Applied Intelligence
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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This paper considers the problem of mining closed frequent itemsets over a 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 consist of 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 previous approaches.