A survey on algorithms for mining frequent itemsets over data streams

@article{Cheng2007ASO,
  title={A survey on algorithms for mining frequent itemsets over data streams},
  author={James Cheng and Yiping Ke and Wilfred Ng},
  journal={Knowledge and Information Systems},
  year={2007},
  volume={16},
  pages={1-27}
}
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI… CONTINUE READING
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