Traditional frequent itemset mining (FIM) is constrained by several limitations, mainly due to its failure to account for item quantity and significance, including factors such as price and profit. To address these limitations, high utility itemset mining (HUIM) is presented. Traditional HUIM algorithms are designed to operate solely on static transactional datasets. Nevertheless, in practical applications, datasets tend to be dynamic, with examples like market basket analysis and business decision-making involving regular updates to the data. Dynamic datasets are updated incrementally with the frequent addition of new data. Incremental HUIM (iHUIM) approaches mine the high utility itemsets (HUIs) from incremental datasets without scanning the whole dataset. In contrast, traditional HUIM approaches require a full dataset scan each time the dataset is updated. Consequently, iHUIM approaches effectively reduce the computational cost of identifying HUIs whenever a new record is added. This survey provides a novel taxonomy that includes two-based, pattern-growth-based, projection-based, utility-list-based, and pre-large-based algorithms. The paper delivers an in-depth analysis, covering the features and characteristics of the existing state-of-the-art algorithms. Additionally, it supplies a detailed comparative overview, advantages, disadvantages, and future research directions of these algorithms. The survey provides both a categorized analysis and a comprehensive, consolidated summary and analysis of all current state-of-the-art iHUIM algorithms. It offers a more in-depth comparative analysis than the currently available state-of-the-art surveys. Additionally, the survey highlights several research opportunities and future directions for iHUIM.
