Abstract

This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when they are removed from the sliding window. It exploits a compact data structure to maintain potentially frequent itemsets so that it can output recent frequent itemsets at any time. Flexible queries for continuous transactions in the sliding window can be answered with an error bound guarantee.

Keywords

MSC

68P20, 68U35, 68P30, 68P10

Cite this article

@article{cai-1681,
  author  = {Congying Han and Lijun Xu and Guoping He},
  title   = {Mining Recent Frequent Itemsets in Sliding Windows over Data Streams},
  journal = {Computing and Informatics},
  volume  = {27},
  number  = {3},
  pages   = {315--339},
  year    = {2008},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2008/3/mining-recent-frequent-itemsets-in-sliding-windows-over-data-streams/}
}
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