doi:10.1017/S0269888925000013

Agrawal , R., Imieliński, T. & Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD’93, 207–216. Association for Computing Machinery. https://doi.org/10.1145/170035.170072

Ahmed , C. F., Tanbeer , S. K., Jeong , B.-S. & Lee , Y.-K. 2009a. Efficient tree structures for high utility pattern mining in incremental databases. IEEE Transactions on Knowledge and Data Engineering 21(12), 1708–1721. http://doi.org/10.1109/TKDE.2009.46

Ahmed , C. F., Tanbeer , S. K., Jeong , B.-S. & Lee , Y.-K. 2009b. Mining high utility patterns in incremental databases. In Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, ICUIMC’09, 656–663. Association for Computing Machinery. https://doi.org/10.1145/1516241.1516357

Cheng , H., Han , M., Zhang , N., Li , X. & Wang , L. 2021. A survey of incremental high-utility pattern mining based on storage structure. Journal of Intelligent & Fuzzy Systems 41(1), 841–866. https://doi.org/10.3233/JIFS-202745

Cheung , D., Han , J., Ng , V. & Wong , C. 1996. Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 1996 IEEE 12th International Conference on Data Engineering; Conference date: 26-02-1996 Through 01-03-1996, 106–114.

Chu , C.-J., Tseng , V. S. & Liang , T. 2009. An efficient algorithm for mining high utility itemsets with negative item values in large databases. Applied Mathematics and Computation 215(2), 767–778. http://www.sciencedirect.com/science/article/pii/S009630030900561X

Dam , T.-L., Li , K., Fournier-Viger , P. & Duong , Q.-H. 2018. CLS-Miner: efficient and effective closed high-utility itemset mining. Frontiers of Computer Science 13, 357–381.

Dam , T.-L., Ramampiaro , H., Nørvåg , K. & Duong , Q.-H. 2019. Towards efficiently mining closed high utility itemsets from incremental databases. Knowledge-Based Systems 165, 13–29. https://www.sciencedirect.com/science/article/pii/S0950705118305719

Fournier Viger , P., Gomariz , A., Campos , M. & Thomas , R. 2014. Fast vertical mining of sequential patterns using co-occurrence information, 40–52.

Fournier-Viger , P., Lin , J. C.-W., Gueniche , T. & Barhate , P. 2015. Efficient incremental high utility itemset mining. In Proceedings of the ASE BigData & SocialInformatics 2015, ASE BD&SI’15, 53:1–53:6. ACM. http://doi.org/10.1145/2818869.2818887

Fournier-Viger , P., Wu , C.-W., Zida , S. & Tseng , V. S. 2014. FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning, 83–92. Springer International Publishing. http://doi.org/10.1007/978-3-319-08326-1_9

Fournier-Viger , P., Zida , S., Lin , J. C.-W., Wu , C.-W. & Tseng , V. S. 2016. EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets, 199–213. Springer International Publishing. http://doi.org/10.1007/978-3-319-41920-6_15

Gan , W., Chun-Wei , J., Chao , H.-C., Wang , S.-L. & Yu , P. S. 2018. Privacy preserving utility mining: A survey. In 2018 IEEE International Conference on Big Data (Big Data), 2617–2626.

Gan , W., Lin , J. C.-W., Fournier-Viger , P., Chao , H.-C., Hong , T.-P. & Fujita , H. 2018. A survey of incremental high-utility itemset mining. WIREs Data Mining and Knowledge Discovery 8(2), e1242. https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1242

Guo , S. & Gao , H. 2017. An efficient algorithm for incremental and interactive high utility itemset mining. In 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 996–1001.

Han , J., Pei , J. & Yin , Y. 2000. Mining frequent patterns without candidate generation. In ACM Sigmod Record, 29, 1–12. ACM.

Han , J., Pei , J., Yin , Y. & Mao , R. 2004. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87.

Hong , T.-P., Lee , C.-H. & Wang , S.-L. 2009a. An incremental mining algorithm for high average-utility itemsets. In 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, 421–425.

Hong , T.-P., Lee , C.-H. & Wang , S.-L. 2009b. Mining high average-utility itemsets. In 2009 IEEE International Conference on Systems, Man and Cybernetics, 2526–2530.

Hong , T.-P., Lee , C.-H. & Wang , S.-L. 2011. Effective utility mining with the measure of average utility. Expert Systems with Applications 38(7), 8259–8265. https://www.sciencedirect.com/science/article/pii/S0957417411000261

Hong , T.-P., Lin , C.-W. & Wu , Y.-L. 2008. Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34, 2424–2435.

Hong , T.-P., Wang , C.-Y. & Tao , Y.-H. 2001. A new incremental data mining algorithm using pre-large itemsets. Intelligent Data Analysis 5, 111–129.

Kim , D. & Yun , U. 2016. Efficient mining of high utility pattern with considering of rarity and length. Applied Intelligence 45(1), 152–173. https://doi.org/10.1007/s10489-015-0750-2

Kim , D. & Yun , U. 2017. Efficient algorithm for mining high average-utility itemsets in incremental transaction databases. Applied Intelligence 47(1), 114–131. https://doi.org/10.1007/s10489-016-0890-z

Kim , H., Lee , C., Ryu , T., Kim , H., Kim , S., Vo , B., Lin , J. C.-W. & Yun , U. 2023. Pre-large based high utility pattern mining for transaction insertions in incremental database. Knowledge-Based Systems 268(C). https://doi.org/10.1016/j.knosys.2023.110478

Kim , H., Ryu , T., Lee , C., Kim , H., Yoon , E., Vo , B., Chun-Wei Lin , J. & Yun , U. 2022. EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits. Expert Systems with Applications 209, 118214. https://www.sciencedirect.com/science/article/pii/S0957417422013689

Koh , J.-L. & Shieh , S.-F. 2004. An efficient approach for maintaining association rules based on adjusting fp-tree structures. In Database Systems for Advanced Applications, Lee , Y., Li , J., Whang , K.-Y. & Lee , D. (eds), 417–424. Springer Berlin Heidelberg.

Krishnamoorthy , S. 2015. Pruning strategies for mining high utility itemsets. Expert Systems with Applications 42(5), 2371–2381. http://www.sciencedirect.com/science/article/pii/S0957417414006848

Kumar , R. & Singh , K. 2022. A survey on soft computing-based high-utility itemsets mining. Soft Computing 26(13), 6347–6392. https://doi.org/10.1007/s00500-021-06613-4

Lee , J., Yun , U., Lee , G. & Yoon , E. 2018. Efficient incremental high utility pattern mining based on pre-large concept. Engineering Applications of Artificial Intelligence 72(C), 111–123. https://doi.org/10.1016/j.engappai.2018.03.020

Li , Y.-C., Yeh , J.-S. & Chang , C.-C. 2005a. Direct candidates generation: A novel algorithm for discovering complete share-frequent itemsets. In Proceedings of the Second International Conference on Fuzzy Systems and Knowledge Discovery - Volume Part II, FSKD’05, 551–560. Springer-Verlag. https://doi.org/10.1007/11540007_67

Li , Y.-C., Yeh , J.-S. & Chang , C.-C. 2005b. A fast algorithm for mining share-frequent itemsets. In Proceedings of the 7th Asia-Pacific Web Conference on Web Technologies Research and Development, APWeb’05, 417–428. Springer-Verlag. https://doi.org/10.1007/978-3-540-31849-1_41

Li , Y.-C., Yeh , J.-S. & Chang , C.-C. 2005c. A fast algorithm for mining share-frequent itemsets. In Web Technologies Research and Development - APWeb 2005, Y. Zhang , K. Tanaka , J. X. Yu , S. Wang & M. Li , (eds), 417–428. Springer Berlin Heidelberg.

Li , Y.-C., Yeh , J.-S. & Chang , C.-C. 2008. Isolated items discarding strategy for discovering high utility itemsets. Data and Knowledge Engineering 64(1), 198–217. http://www.sciencedirect.com/science/article/pii/S0169023X07001218

Lin , C.-W. & Hong , T.-P. 2014. Maintenance of prelarge trees for data mining with modified records. Information Sciences 278(Complete), 88–103.

Lin , C.-W., Hong , T.-P., Gan , W., Chen , H.-Y. & Li , S.-T. 2015. Incrementally updating the discovered sequential patterns based on pre-large concept. Intelligent Data Analysis 19, 1071–1089.

Lin , C.-W., Hong , T.-P., Lan , G.-C., Wong , J.-W. & Lin , W.-Y. 2014. Incrementally mining high utility patterns based on pre-large concept. Applied Intelligence 40(2), 343–357. https://doi.org/10.1007/s10489-013-0467-z

Lin , C.-W., Lan , G.-C. & Hong , T.-P. 2012. An incremental mining algorithm for high utility itemsets. Expert Systems with Applications 39(8), 7173–7180. https://doi.org/10.1016/j.eswa.2012.01.072

Lin , J. C.-W., Gan , W., Fournier-Viger , P. & Hong , T.-P. 2015. Mining high-utility itemsets with multiple minimum utility thresholds. In Proceedings of the Eighth International C Conference on Computer Science and Software Engineering, C3S2E’15, 9–17. Association for Computing Machinery. https://doi.org/10.1145/2790798.2790807

Lin , J. C.-W., Gan , W. & Hong , T.-P. 2015. A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification. Advanced Engineering Informatics 29(3), 562–574. https://doi.org/10.1016/j.aei.2015.05.003

Lin , J. C.-W., Gan , W., Hong , T.-P. & Pan , J.-S. 2014. Incrementally Updating High-Utility Itemsets with Transaction Insertion. Springer International Publishing, 44–56. https://doi.org/10.1007/978-3-319-14717-8_4

Lin , J. C.-W., Gan , W., Hong , T.-P. & Zhang , B. 2015. An incremental high-utility mining algorithm with transaction insertion. The Scientific World Journal 2015.

Lin , J. C.-W., Li , T., Fournier-Viger , P., Hong , T.-P., Zhan , J. & Voznak , M. 2016. An efficient algorithm to mine high average-utility itemsets. Advanced Engineering Informatics 30(2), 233–243. http://www.sciencedirect.com/science/article/pii/S1474034616300507

Lin , J. C.-W., Pirouz , M., Djenouri , Y., Cheng , C.-F. & Ahmed , U. 2020. Incrementally updating the high average-utility patterns with pre-large concept. Applied Intelligence 50(11), 3788–3807.

Lin , J. C.-W., Ren , S., Fournier-Viger , P., Pan , J.-S. & Hong , T.-P. 2018. Efficiently updating the discovered high average-utility itemsets with transaction insertion. Engineering Applications of Artificial Intelligence 72(C), 136–149. https://doi.org/10.1016/j.engappai.2018.03.021

Lin , M.-Y., Hsueh , S.-C. & Chan , C.-C. 2009. Incremental discovery of sequential patterns using a backward mining approach. In 2009 International Conference on Computational Science and Engineering 1, 64–70.

Liu , J., Ju , X., Zhang , X., Fung , B. C. M., Yang , X. & Yu , C. 2019. Incremental mining of high utility patterns in one phase by absence and legacy-based pruning. IEEE Access 7, 74168–74180.

Liu , J., Wang , K. & Fung , B. C. M. 2012. Direct discovery of high utility itemsets without candidate generation. In 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, 984–989.

Liu , J., Wang , K. & Fung , B. C. M. 2016. Mining high utility patterns in one phase without generating candidates. IEEE Transactions on Knowledge and Data Engineering 28(5), 1245–1257.

Liu , M. & Qu , J. 2012. Mining high utility itemsets without candidate generation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM’12, 55–64. ACM. http://doi.acm.org/10.1145/2396761.2396773

Liu , Y., Liao , W.-k. & Choudhary, A. 2005. A two-phase algorithm for fast discovery of high utility itemsets. In Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD’05, 689–695. Springer-Verlag. http://doi.org/10.1007/11430919_79

Pushp & Chand, S. 2021. Mining of high utility itemsets for incremental datasets. In 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 01–06.

Saleti , S. 2021. Incremental mining of high utility sequential patterns using MapReduce paradigm. Cluster Computing 25, 805–825.

Saleti , S. & Subramanyam , R. 2019. A MapReduce solution for incremental mining of sequential patterns from big data. Expert Systems with Applications 133, 109–125.

Shao , J., Meng , X. & Cao , L. 2016. Mining actionable combined high utility incremental and associated patterns. In 2016 IEEE International Conference on Aircraft Utility Systems (AUS), 1164–1169.

Singh , K., Kumar , R. & Biswas , B. 2022. High average-utility itemsets mining: a survey. Applied Intelligence 52(4), 3901–3938. https://doi.org/10.1007/s10489-021-02611-z

Singh , K., Singh , S. S., Kumar , A. & Biswas , B. n.d. High utility itemsets mining with negative utility value: A survey. Journal of Intelligent & Fuzzy Systems 35(6), 6551–6562.

Singh , K., Singh , S. S., Luhach , A. K., Kumar , A. & Biswas , B. 2021. Mining of closed high utility itemsets: A survey. Recent Advances in Computer Science and Communications 14(1), 6–12. http://www.eurekaselect.com/article/96348

Srivastava , G., Lin , J. C.-W., Zhang , X. & Li , Y. 2021. Large-scale high-utility sequential pattern analytics in Internet of Things. IEEE Internet of Things Journal 8(16), 12669–12678.

Tseng , V. S., Shie , B.-E., Wu , C.-W. & Yu , P. S. 2013. Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Transactions on Knowledge and Data Engineering 25(8), 1772–1786. http://doi.org/10.1109/TKDE.2012.59

Tseng , V. S., Wu , C. W., Fournier-Viger , P. & Yu , P. S. 2016. Efficient algorithms for mining top-k high utility itemsets. IEEE Transactions on Knowledge and Data Engineering 28(1), 54–67.

Tseng , V. S., Wu , C.-W., Shie , B.-E. & Yu , P. S. 2010. UP-Growth: An efficient algorithm for high utility itemset mining. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’10, 253–262. ACM. http://doi.acm.org/10.1145/1835804.1835839

Wang , J.-Z. & Huang , J.-L. 2016. Incremental mining of high utility sequential patterns in incremental databases. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM’16, 2341–2346. Association for Computing Machinery. https://doi.org/10.1145/2983323.2983691

Wang , J.-Z. & Huang , J.-L. 2018. On incremental high utility sequential pattern mining. ACM Transactions on Intelligent Systems and Technology 9(5). https://doi.org/10.1145/3178114

Wang , J.-Z., Huang , J.-L. & Chen , Y.-C. 2016. On efficiently mining high utility sequential patterns. Knowledge and Information Systems 49(2), 597–627. https://doi.org/10.1007/s10115-015-0914-8

Wu , C. W., Fournier-Viger , P., Gu , J. Y. & Tseng , V. S. 2015. Mining closed+ high utility itemsets without candidate generation. In 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 187–194.

Wu , J. M.-T., Teng , Q., Lin , C.-W., Yun , U. & Chen , H.-C. 2020a. Updating high average-utility itemsets with pre-large concept. Journal of Intelligent and Fuzzy Systems 38, 1–10.

Wu , J. M.-T., Teng , Q., Lin , J. C.-W. & Cheng , C.-F. 2020b. Incrementally updating the discovered high average-utility patterns with the pre-large concept. IEEE Access 8, 66788–66798.

Wu , J. M.-T., Teng , Q., Lin , J. C.-W., Fournier-Viger , P. & Cheng , C.-F. 2020c. Maintenance of prelarge high average-utility patterns in incremental databases. In Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices, Fujita , H., Fournier-Viger , P., Ali , M. & Sasaki , J. (eds), 884–895. Springer International Publishing.

Yao , H. & Hamilton , H. J. 2006. Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626. http://doi.org/10.1016/j.datak.2005.10.004

Yeh , J.-S., Chang , C.-Y. & Wang , Y.-T. 2008. Efficient algorithms for incremental utility mining. In Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, ICUIMC’08, 212–217. Association for Computing Machinery. https://doi.org/10.1145/1352793.1352839

Yen , S.-J., Lee , Y.-S. & Hsieh, M. 2005. An efficient incremental algorithm for mining web traversal patterns. In Proceedings of the IEEE International Conference on E-Business Engineering, IEEE Computer Society, USA, 274–281. https://doi.org/10.1109/ICEBE.2005.25

Yildirim , I. & Celik , M. 2018. FIMHAUI: fast incremental mining of high average-utility itemsets. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1–9.

Yin , J., Zheng , Z. & Cao , L. 2012. USpan: an efficient algorithm for mining high utility sequential patterns. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’12, 660–668. ACM. http://doi.acm.org/10.1145/2339530.2339636

Yin , J., Zheng , Z., Cao , L., Song , Y. & Wei , W. 2013. Efficiently mining top-k high utility sequential patterns. In 2013 IEEE 13th International Conference on Data Mining, 1259–1264.

Yun , U., Kim , H., Ryu , T., Baek , Y., Nam , H., Lee , J., Vo , B. & Pedrycz , W. 2021. Prelarge-based utility-oriented data analytics for transaction modifications in internet of things. IEEE Internet of Things Journal 8(24), 17333–17344.

Yun , U., Nam , H., Lee , G. & Yoon , E. 2019. Efficient approach for incremental high utility pattern mining with indexed list structure. Future Generation Computer Systems 95, 221–239. https://www.sciencedirect.com/science/article/pii/S0167739X18315814

Yun , U. & Ryang , H. 2014. Incremental high utility pattern mining with static and dynamic databases. Applied Intelligence 42, 323–352.

Yun , U., Ryang , H., Lee , G. & Fujita , H. 2017. An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowledge-Based Systems 124(C), 188–206. https://doi.org/10.1016/j.knosys.2017.03.016

Zaki , M. J. 2000. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390.

Zheng , H.-T. & Li , Z. 2015. iCHUM: an efficient algorithm for high utility mining in incremental databases. In Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management - Volume 9403, KSEM 2015, 212–223. Springer-Verlag. https://doi.org/10.1007/978-3-319-25159-2_20

Zihayat , M. & An , A. 2014. Mining top-k high utility patterns over data streams. Information Sciences 285, 138–161. Processing and Mining Complex Data Streams. http://www.sciencedirect.com/science/article/pii/S0020025514000814

Zihayat , M., Hut , Z. Z., An , A. & Hut , Y. 2016. Distributed and parallel high utility sequential pattern mining. In 2016 IEEE International Conference on Big Data (Big Data), 853–862.