STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
Moment+: Mining Closed Frequent Itemsets over Data Stream
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
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
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
IEICE - Transactions on Information and Systems
Data Mining and Knowledge Discovery
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
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
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
GC-tree: a fast online algorithm for mining frequent closed itemsets
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Estimating missing data in data streams
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
TGC-tree: an online algorithm tracing closed itemset and transaction set simultaneously
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
New approach in data stream association rule mining based on graph structure
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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 frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Expert Systems with Applications: An International Journal
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
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
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
A data imputation model in sensor databases
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
Stream mining on univariate uncertain data
Applied Intelligence
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent closed itemsets, but they are mainly intended for traditional transaction databases and thus do not take data stream characteristics into consideration. In this paper, we propose a novel approach for mining closed frequent itemsets over data streams. It computes and maintains closed itemsets online and incrementally, and can output the current closed frequent itemsets in real time based on users' specified thresholds. Experimental results show that our proposed method is both time and space efficient, has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams.