Incremental Skyline Frequent‐Utility Itemset Mining

High frequency and utility are two crucial objectives in data mining, as they provide valuable insights for decision‐making. Most frequent‐utility pattern mining methods rely on threshold‐based approaches, which can inadvertently overlook itemsets with low frequency but high utility, or those with low utility but high frequency. Skyline frequent‐utility 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‐world scenarios, databases typically receive new transactions incrementally, necessitating an efficient algorithm to find skyline frequent‐utility patterns in dynamic environments. This paper introduces the ISFUM (Incremental Skyline Frequent‐Utility Mining) algorithm, which efficiently discovers skyline frequent‐utility itemsets in dynamic transaction databases. By utilising global and local utility‐lists, 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.