Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Estimating simple functions on the union of data streams
Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Incremental maintenance of quotient cube for median
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
MFIS—Mining frequent itemsets on data streams
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
A sliding window based algorithm for frequent closed itemset mining over data streams
Journal of Systems and Software
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In order to mining frequent itemsets on data stream efficiently, a new approach was proposed in this paper. The memory efficient and accurate one-pass algorithm divides all the frequent itemsets into frequent equivalence classes and prune all the redundant itemsets except for those represent the GLB(Greatest Lower Bound) and LUB(Least Upper Bound) of the frequent equivalence class and the number of GLB and LUB is much less than that of frequent itemsets. In order to maintain these equivalence classes, A compact data structure, the frequent itemset enumerate tree (FIET) was proposed in the paper. The detailed experimental evaluation on synthetic and real datasets shows that the algorithm is very accurate in practice and requires significantly lower memory than Jin and Agrawal's one pass algorithm.