In this paper, a new algorithm HCOUNT+ is proposed to find frequent items over data stream based on the HCOUNT algorithm. The new algorithm adopts aided measures to improve the precision of HCOUNT greatly. In addition, HCOUNT+ is introduced to time critical applications and a novel sliding windows-based algorithm SL-HCOUNT+ is proposed to mine the most frequent items occurring recently. This algorithm uses limited memory (nB·(1+α)·eε·ln-M/lnρ(α<1) counters), requires constant processing time per packet (only (1+α)·ln·-M/lnρ(α<1) counters are updated), makes only one pass over the streaming data, and is shown to work well in the experimental results.
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