Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 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
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
DWFIST: leveraging calendar-based pattern mining in data streams
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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We propose an efficient approach to mine frequent Itemsets on data streams. It is a memory efficient and accurate one-pass algorithm that can deal with batch updates. The proposed algorithm performers well by dividing all frequent itemsets into frequent equivalence classes and pruning all redundant itemsets except for those that represent GLB (Greatest Lower Bound) and LUB (Least Upper Bound) of the frequent equivalence classes. The number of GLB and LUB is much less than the number of frequent itemsets. The experimental evaluation on synthetic and real datasets shows that the algorithm is very accurate and requires significantly lower memory than other well-known one-pass algorithms.