Dynamic Association Mining Techniques for the Faster Extraction of High Utility Itemsets from Incremental Databases
Subba Reddy Meruva, Bondu Venkateswarlu · Engineering Technology & Applied Science Research · 2025
Financial and market analysis applications require the mining of strong-utility itemsets. Finding frequent itemsets with high utility patterns is vital for such wide applications. Recent utility-based mining methods were successfully used in the current study to identify high value itemsets from static datasets. Stream databases or incremental databases update the itemsets at regular intervals (schedulers). Incremental Mining-based High Utility Itemset (IM-HUI) algorithms improve the methodologies based on High Utility Itemset (HUI) methods. The proposed technique refines the itemset values and updates the HUIs based on incremental schedulers. It reduces the time and space while mining HUIs over dynamic databases. The efficacy of the proposed work is compared experimentally to that of existing mining techniques on benchmark datasets.