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Most frequent\u2010utility pattern mining methods rely on threshold\u2010based approaches, which can inadvertently overlook itemsets with low frequency but high utility, or those with low utility but high frequency. Skyline frequent\u2010utility pattern mining addresses this limitation by capturing patterns that are optimal across both dimensions. However, existing skyline algorithms are primarily designed for static databases. In real\u2010world scenarios, databases typically receive new transactions incrementally, necessitating an efficient algorithm to find skyline frequent\u2010utility patterns in dynamic environments. This paper introduces the ISFUM (Incremental Skyline Frequent\u2010Utility Mining) algorithm, which efficiently discovers skyline frequent\u2010utility itemsets in dynamic transaction databases. By utilising global and local utility\u2010lists, ISFUM avoids rescanning the original database during incremental updates. Additionally, we have developed novel pruning strategies to further reduce computational time by minimising unnecessary candidate validation. Experimental results demonstrate that our algorithm is both efficient and effective in handling dynamic data environments.<\/jats:p>","DOI":"10.1111\/exsy.70236","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:57Z","timestamp":1773315957000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incremental Skyline Frequent\u2010Utility Itemset Mining"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-098X","authenticated-orcid":false,"given":"Xiaojie","family":"Zhang","sequence":"first","affiliation":[{"name":"Cyberspace Institute of Advanced Technology Guangzhou University  Guangzhou China"},{"name":"City University of Hong Kong  Hong Kong China"}]},{"given":"Guoting","family":"Chen","sequence":"additional","affiliation":[{"name":"Great\u00a0Bay University  Dongguan China"}]},{"given":"Linqi","family":"Song","sequence":"additional","affiliation":[{"name":"City University of Hong Kong  Hong Kong China"}]},{"given":"Wensheng","family":"Gan","sequence":"additional","affiliation":[{"name":"Jinan University  Guangzhou China"}]}],"member":"311","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"487","volume-title":"International Conference on Very Large Data Bases","author":"Agrawal R.","year":"1994"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.46"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1942517"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.041"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103735"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","unstructured":"B\u00f6rzs\u00f6nyi S. 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