Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation
High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential patt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12340 |
| _version_ | 1827640698620346368 |
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| author | Kai Cao Yucong Duan |
| author_facet | Kai Cao Yucong Duan |
| author_sort | Kai Cao |
| collection | DOAJ |
| description | High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-<i>k</i> high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance. |
| first_indexed | 2024-03-09T17:03:17Z |
| format | Article |
| id | doaj.art-30ac9390eb774963968a9cdd7ee12bbe |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:03:17Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-30ac9390eb774963968a9cdd7ee12bbe2023-11-24T14:27:20ZengMDPI AGApplied Sciences2076-34172023-11-0113221234010.3390/app132212340Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource TransformationKai Cao0Yucong Duan1School of Cyberspace Security, Hainan University, Renmin Avenue 58, Haikou 570228, ChinaSchool of Computer Science and Technology, Hainan University, Renmin Avenue 58, Haikou 570228, ChinaHigh-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-<i>k</i> high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.https://www.mdpi.com/2076-3417/13/22/12340DIKW graphresource transformationsequential patternaverage utilitytop-<i>k</i>pattern mining |
| spellingShingle | Kai Cao Yucong Duan Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation Applied Sciences DIKW graph resource transformation sequential pattern average utility top-<i>k</i> pattern mining |
| title | Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation |
| title_full | Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation |
| title_fullStr | Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation |
| title_full_unstemmed | Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation |
| title_short | Mining Top-<i>k</i> High Average-Utility Sequential Patterns for Resource Transformation |
| title_sort | mining top i k i high average utility sequential patterns for resource transformation |
| topic | DIKW graph resource transformation sequential pattern average utility top-<i>k</i> pattern mining |
| url | https://www.mdpi.com/2076-3417/13/22/12340 |
| work_keys_str_mv | AT kaicao miningtopikihighaverageutilitysequentialpatternsforresourcetransformation AT yucongduan miningtopikihighaverageutilitysequentialpatternsforresourcetransformation |