期刊论文详细信息
IEEE Access
Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning
Xinyi Ju1  Xingxing Zhang1  Changhong Yu1  Junqiang Liu1  Xiangcai Yang1  Benjamin C. M. Fung2 
[1] Department of Information and Electrical Engineering, Zhejiang Gongshang University, Hangzhou, China;School of Information Studies, McGill University, Montreal, Canada;
关键词: Data mining;    utility mining;    high utility patterns;    pattern mining;    dynamic databases;   
DOI  :  10.1109/ACCESS.2019.2919524
来源: DOAJ
【 摘 要 】

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 (Id2HUP+). Id2HUP+ 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.

【 授权许可】

Unknown   

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