Damped weighted erasable itemset mining with time sensitive dynamic environments
Abstract Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01056-8 |
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| _version_ | 1832571605514977280 |
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| author | Hanju Kim Myungha Cho Seungwan Park Doyoung Kim Doyoon Kim Unil Yun |
| author_facet | Hanju Kim Myungha Cho Seungwan Park Doyoung Kim Doyoon Kim Unil Yun |
| author_sort | Hanju Kim |
| collection | DOAJ |
| description | Abstract Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing studies on weighted erasable itemset mining do not regard the time-sensitivity of data arrival times. In this paper, we propose a new weighted erasable itemset mining approach considering time-sensitive dynamic environments. For industrial manufacturers with automated control systems, our method focuses on recent data and item weights to effectively filter out unprofitable itemsets. Performance tests show that the proposed method outperforms state-of-the-art studies in runtime with on-par or minimal compromise in memory usage and that it scales capably with varying database sizes. We performed statistical analyses to demonstrate the correctness and significance of the discovered results from our proposed method. Furthermore, extended evaluations on sensitivity and resultant itemsets show how the algorithm responds to varying parameters as well as provide insights on the discovered itemsets. |
| format | Article |
| id | doaj-art-9f5f2e73d96a4e4eb7bceec030c611ee |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-9f5f2e73d96a4e4eb7bceec030c611ee2025-02-02T12:28:28ZengSpringerOpenJournal of Big Data2196-11152025-01-0112113810.1186/s40537-024-01056-8Damped weighted erasable itemset mining with time sensitive dynamic environmentsHanju Kim0Myungha Cho1Seungwan Park2Doyoung Kim3Doyoon Kim4Unil Yun5Department of Computer Engineering, Sejong UniversityDepartment of Computer Engineering, Sejong UniversityDepartment of Computer Engineering, Sejong UniversityDepartment of Computer Engineering, Sejong UniversityDepartment of Computer Engineering, Sejong UniversityDepartment of Computer Engineering, Sejong UniversityAbstract Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing studies on weighted erasable itemset mining do not regard the time-sensitivity of data arrival times. In this paper, we propose a new weighted erasable itemset mining approach considering time-sensitive dynamic environments. For industrial manufacturers with automated control systems, our method focuses on recent data and item weights to effectively filter out unprofitable itemsets. Performance tests show that the proposed method outperforms state-of-the-art studies in runtime with on-par or minimal compromise in memory usage and that it scales capably with varying database sizes. We performed statistical analyses to demonstrate the correctness and significance of the discovered results from our proposed method. Furthermore, extended evaluations on sensitivity and resultant itemsets show how the algorithm responds to varying parameters as well as provide insights on the discovered itemsets.https://doi.org/10.1186/s40537-024-01056-8Data miningAutomated control systemWeighted erasable itemset miningDamped windowLoss prevention |
| spellingShingle | Hanju Kim Myungha Cho Seungwan Park Doyoung Kim Doyoon Kim Unil Yun Damped weighted erasable itemset mining with time sensitive dynamic environments Journal of Big Data Data mining Automated control system Weighted erasable itemset mining Damped window Loss prevention |
| title | Damped weighted erasable itemset mining with time sensitive dynamic environments |
| title_full | Damped weighted erasable itemset mining with time sensitive dynamic environments |
| title_fullStr | Damped weighted erasable itemset mining with time sensitive dynamic environments |
| title_full_unstemmed | Damped weighted erasable itemset mining with time sensitive dynamic environments |
| title_short | Damped weighted erasable itemset mining with time sensitive dynamic environments |
| title_sort | damped weighted erasable itemset mining with time sensitive dynamic environments |
| topic | Data mining Automated control system Weighted erasable itemset mining Damped window Loss prevention |
| url | https://doi.org/10.1186/s40537-024-01056-8 |
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