Abstract
To overcome drawbacks of traditional pattern mining such as difficulty reflecting characteristics of real-world databases to pattern mining, high utility pattern mining has been proposed and researched. Since database sizes become larger incrementally in many real-world applications, there is a need of appropriate methods to deal with such databases for discovering useful information from them efficiently. For this purpose, various approaches have been suggested. In this paper, we compare and analyze algorithms for high utility pattern mining from dynamic databases by considering characteristics of incremental databases and utilizing tree-based data structures. Moreover, we study their characteristics and direction of improvements based on experimental results of performance evaluation.
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Acknowledgments
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 20152062051 and NRF No. 20155054624).
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Ryang, H., Yun, U. (2017). Performance Analysis of Tree-Based Algorithms for Incremental High Utility Pattern Mining. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_20
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DOI: https://doi.org/10.1007/978-981-10-3023-9_20
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