Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Temporal Aggregation over Data Streams Using Multiple Granularities
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
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
Approximate frequency counts over data streams
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
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Recently, due to technical improvements of storage devices and networks, the amount of data increases rapidly. In addition, it is required to find the knowledge embedded in a data stream as fast as possible. Data stream is influenced by time. Therefore, the itemsets which were not the frequent itemsets can become frequent itemsets. The volume of data stream is so large that it can hardly be stored in finite memory space. Current researches do not offer appropriate method to find frequent itemsets in which flow of time is reflected but provide only frequent items using total aggregation values. In this paper we propose a novel algorithm for finding the relative frequent itemsets according to the time in a data stream. We also propose a method to save frequent items and sub-frequent items in order to take limited memory into account and a method to update time variant frequent items. By applying the proposed technique, we can improve the accuracy of searching for a change in the frequent itemsets according to the time in a data stream. Moreover, it will be able to use the limited memory space efficiently and store all frequent itemsets.