Mining frequent items in the time fading model
Mining frequent items in the time fading model
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DOI：
10.1016/j.ins.2016.07.077
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发表时间：
2016-11-20
影响因子：
8.1
通讯作者：
Aloisio, Giovanni
中科院分区：
文献类型：
--
作者：
Cafaro, Massimo;Pulimeno, Marco;Aloisio, Giovanni
提出了一种基于草图的数据流频繁项挖掘算法FDCMSS。该算法巧妙地结合了向前衰减，计数最小和节省空间算法的关键思想。它工作在时间衰落模型下，根据收银机模型挖掘数据流。我们正式证明了它的正确性，并显示，通过大量的实验结果，我们的算法优于HECount，最近开发的算法，在速度，空间使用，精度达到和错误的合成和真实的数据集。(C)2016 Elsevier Inc. All rights reserved.
We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms lambda-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets. (C) 2016 Elsevier Inc. All rights reserved.