Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database Article Swipe
Chanhee Lee
,
Hanju Kim
,
Myungha Cho
,
Hyeonmo Kim
,
Bay Vo
,
Jerry Chun‐Wei Lin
,
Philippe Fournier‐Viger
,
Unil Yun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3406562
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3406562
Top-k high utility pattern mining, which extracts the highest top-k patterns that the users want to find, has been actively studied. Most previous studies in this domain have focused on static databases, where data insertions do not occur. In the real world, however, various applications continuously generate new data, and existing top-k high utility pattern mining algorithms devised to process static databases cannot handle incremental databases. Although some methods can handle stream data, they have the limitation of processing a portion of the database rather than the entire accumulated database. In this paper, we suggest an efficient incremental mining method that discovers top-k high utility patterns from the entire accumulated database. The proposed approach utilizes a list structure that stores minimal utility information required for the mining process and does not generate candidate itemsets. The suggested algorithm processes the incremental data with a single database scan and restructures the list for efficient mining. Moreover, four efficient threshold raising techniques along with a restoring technique are utilized to calculate the optimal threshold value in an accumulated incremental environment. The results of the experiments on runtime, memory, and scalability show that the suggested method efficiently processes the entire incremental database.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3406562
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10540107.pdf
- OA Status
- gold
- Cited By
- 10
- References
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- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4399110533Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2024.3406562Digital Object Identifier
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Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic DatabaseWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Chanhee Lee, Hanju Kim, Myungha Cho, Hyeonmo Kim, Bay Vo, Jerry Chun‐Wei Lin, Philippe Fournier‐Viger, Unil YunList of authors in order
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https://doi.org/10.1109/access.2024.3406562Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10540107.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10540107.pdfDirect OA link when available
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Computer science, Database, Data mining, Process (computing), Information retrieval, Programming languageTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 9, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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