期刊
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
卷 37, 期 9, 页码 5477-5507出版社
WILEY
DOI: 10.1002/int.22799
关键词
data mining; high-utility occupancy pattern; incremental database; pattern mining
资金
- national research foundation of korea [2021R1A2C1009388]
- National Research Foundation of Korea [2021R1A2C1009388] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. This article proposes a novel mining approach, HUOMI, which performs quickly on an increasing database and demonstrates better performance compared to other state-of-the-art algorithms.
Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real-time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state-of-the-art algorithms.
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