期刊
IEEE ACCESS
卷 7, 期 -, 页码 74168-74180出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2919524
关键词
Data mining; utility mining; high utility patterns; pattern mining; dynamic databases
资金
- Zhejiang Natural Science Foundation of China [LY17F020004]
- Key Projects of Zhejiang Science and Technology Plan of China [2018C01084]
- First Class Discipline of Zhejiang - A (Zhejiang Gongshang University- Statistics)
- National Natural Science Foundation of China [61272306]
Mining high utility patterns in dynamic databases is an important data mining task. While a naive approach is to mine a newly updated database in its entirety, the state-of-the-art mining algorithms all take an incremental approach. However, the existing incremental algorithms either take a two-phase paradigm that generates a large number of candidates that causes scalability issues or employ a vertical data structure that incurs a large number of join operations that leads to efficiency issues. To address the challenges with the existing incremental algorithms, this paper proposes a new algorithm incremental direct discovery of high utility patterns (Id(2)HUP+). Id(2)HUP+ adapts a one-phase paradigm by improving the relevance-based pruning and upper-bound-based pruning proposes a novel data structure for a quick update of dynamic databases and proposes the absence-based pruning and legacy-based pruning dedicated to incremental mining. The extensive experiments show that our algorithm is up to 1-3 orders of magnitude more efficient than the state-of-the-art algorithms, and is the most scalable algorithm.
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