Incremental high average-utility itemset mining: survey and challenges
Abstract The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and bus...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-60279-0 |
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| author | Jing Chen Shengyi Yang Weiping Ding Peng Li Aijun Liu Hongjun Zhang Tian Li |
| author_facet | Jing Chen Shengyi Yang Weiping Ding Peng Li Aijun Liu Hongjun Zhang Tian Li |
| author_sort | Jing Chen |
| collection | DOAJ |
| description | Abstract The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm. |
| format | Article |
| id | doaj-art-a503580484b3424fa00283db9cc037c7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a503580484b3424fa00283db9cc037c72024-11-24T12:25:06ZengNature PortfolioScientific Reports2045-23222024-04-0114112510.1038/s41598-024-60279-0Incremental high average-utility itemset mining: survey and challengesJing Chen0Shengyi Yang1Weiping Ding2Peng Li3Aijun Liu4Hongjun Zhang5Tian Li6School of Internet of Things, Nanjing University of Posts and TelecommunicationsSchool of Physics and Mechatronic Engineering, Guizhou Minzu UniversitySchool of Information Science and Technology, Nantong UniversitySchool of Computer Science, Nanjing University of Posts and TelecommunicationsBaotou Teachers’ College of Inner Mongolia University of Science and TechnologySchool of Internet of Things, Nanjing University of Posts and TelecommunicationsSchool of Computer and Software, Nanjing Vocational University of Industry TechnologyAbstract The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.https://doi.org/10.1038/s41598-024-60279-0Dynamic data miningHigh Utility Item MiningHigh Average Utility Item MiningPattern mining |
| spellingShingle | Jing Chen Shengyi Yang Weiping Ding Peng Li Aijun Liu Hongjun Zhang Tian Li Incremental high average-utility itemset mining: survey and challenges Scientific Reports Dynamic data mining High Utility Item Mining High Average Utility Item Mining Pattern mining |
| title | Incremental high average-utility itemset mining: survey and challenges |
| title_full | Incremental high average-utility itemset mining: survey and challenges |
| title_fullStr | Incremental high average-utility itemset mining: survey and challenges |
| title_full_unstemmed | Incremental high average-utility itemset mining: survey and challenges |
| title_short | Incremental high average-utility itemset mining: survey and challenges |
| title_sort | incremental high average utility itemset mining survey and challenges |
| topic | Dynamic data mining High Utility Item Mining High Average Utility Item Mining Pattern mining |
| url | https://doi.org/10.1038/s41598-024-60279-0 |
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