Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining concept-drifting data streams using ensemble classifiers
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
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth 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
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Maintaining frequent itemsets over high-speed data streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Frequent pattern mining has emerged as an important mining task in data stream mining. A number of algorithms have been proposed. These algorithms usually use a method of two steps: one is calculating the frequency of itemsets while monitoring each arrival of the data stream, and the other is to output the frequent itemsets according to user's requirement. Due to the large number of item combinations for each transaction occurred in data stream, the first step costs lots of time. Therefore, for high speed long transaction data streams, there may be not enough time to process every transactions arrived in stream, which will reduce the mining accuracy. In this paper, we propose a new approach to deal with this issue. Our new approach is a kind of lazy approach, which delays calculation of the frequency of each itemset to the second step. So, the first step only stores necessary information for each transaction, which can avoid missing any transaction arrival in data stream. In order to improve accuracy, we propose monitoring items which are most likely to be frequent. By this method, many candidate itemsets can be pruned, which leads to the good performance of the algorithm, DELAY, designed based on this method. A comprehensive experimental study shows that our algorithm achieves some improvements over existing algorithms, LossyCountingand FDPM, especially for long transaction data streams.