Journal
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Volume 36, Issue 10, Pages 5741-5769Publisher
WILEY
DOI: 10.1002/int.22528
Keywords
average utility driven data analytics; damped window; data mining; data streams
Categories
Funding
- National Research Foundation of Korea [2021R1A2C1009388, 2018R1D1A1A09083109]
- National Research Foundation of Korea [2018R1D1A1A09083109, 2021R1A2C1009388] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This paper proposes a damped window based average utility driven data analytics method, which improves mining efficiency by modifying the importance of items and without generating candidate patterns. Experimental results show that the proposed method outperforms other techniques in terms of runtime and memory usage, and maintains stable performance under various environmental changes.
In industrial areas, most of databases are dynamic databases, and the volume of the databases has grown with the passage of time. Especially, pattern mining for incremental database needs different approaches from static database because the profit or the accuracy of the previously inserted data can be reduced. Since data is time- sensitive, the recent data has a relatively higher value than the old data. In this paper, we suggest the damped window based average utility driven data analytics for intelligent systems, which the damped window reflects the importance according to the arrival time of the transactions. The proposed mining approach adopts novel data structure, which modify the importance of item as the passage of time, and it improves mining efficiency with several pruning strategies and without generating candidate patterns. To evaluate the performance of the proposed mining approach, we conducted various experiments using several real and synthetic data sets. The result of the experiments presented that the suggested method performs better in terms of runtime and memory usage than the other state-of-the-art mining techniques. Moreover, through the scalability experiments, which changed the number of different items or transactions, we verified that the proposed algorithm maintained a stable performance under various environmental changes.
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