ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (11): 2500-2514.doi: 10.7544/issn1000-1239.2021.20200554

• 人工智能 • 上一篇    下一篇



  1. (北方民族大学计算机科学与工程学院 银川 750021) (
  • 出版日期: 2021-11-01
  • 基金资助: 

Closed High Utility Itemsets Mining over Data Stream Based on Sliding Window Model

Cheng Haodong, Han Meng, Zhang Ni, Li Xiaojuan, Wang Le   

  1. (College of Computer Science and Engineering, North Minzu University, Yinchuan 750021)
  • Online: 2021-11-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62062004), the Natural Science Foundation of Ningxia Hui Autonomous Region of China (2020AAC03216), and the Graduate Innovation Project of North Minzu University (YCX20077).

摘要: 从数据流中挖掘高效用项集是一项具有挑战性的任务,因为传入的数据必须在时间和存储内存约束下进行实时处理.数据流挖掘通常会产生大量冗余的项集,为了减少这些无用的项集数量且保证无损压缩,需要挖掘闭合项集,它可以比全集高效用项集的集合小几个数量级.为了解决以上问题,提出一种基于滑动窗口模型的数据流闭合高效用项集挖掘(closed high utility itemsets mining over data stream based on sliding window model, CHUI_DS)算法. 在CHUI_DS中设计了一种新的效用列表结构,该结构在提升批次插入和删除的速度方面非常有效.此外,应用修剪策略来改进闭合项集挖掘过程,消除潜在的低效用候选对象.对真实数据集和合成数据集进行的广泛实验评估显示了该算法的效率以及可行性.就速度而言,它优于先前提出的主要以批处理模式运行的算法. 且它适用于不同大小的滑动窗口,在事务数量等方面具有较强的扩展性.

关键词: 模式挖掘, 数据流挖掘, 闭合高效用项集, 滑动窗口, 效用列表

Abstract: It is a challenging task to mine high utility itemsets from the data stream, because the incoming data stream must be processed in real time within the constraints of time and storage memory. Data stream mining usually generates a large number of redundant itemsets. In order to reduce the number of these useless itemsets and ensure lossless compression of complete high utility itemsets, it is necessary to mine closed itemsets, which can be several orders of magnitude smaller than the collection of complete high utility itemsets. In order to solve the above problem, a high utility itemsets mining algorithm (sliding-window-model-based closed high utility itemsets mining on data stream, CHUI_DS) is proposed to achieve mining closed high utility itemsets on data stream. A new utility-list structure is designed in CHUI_DS, which is very effective in increasing the speed of batch insertion and deletion. In addition, effective pruning strategies are applied to improve the closed itemset mining process and eliminate potential low-utility candidates. Extensive experimental evaluation of the proposed algorithm on real datasets and synthetic datasets shows the efficiency and feasibility of the algorithm. In terms of speed, it is superior to the previously proposed algorithms that mainly run in batch mode. Moreover, it is suitable for sliding windows of different sizes, and has strong scalability in terms of the number of transactions.

Key words: pattern mining, data stream mining, closed high utility itemsets, sliding window, utility list