Enhancing Profitability: Multi-Utility Range Algorithm for High Utility Itemset Mining
Keywords:
Association Rule Mining; High Utility Range; Low Utility Range; Support and Confidence.Abstract
Association rule mining is a critical domain within data mining, aimed at uncovering interesting relationships between variables in large datasets. Traditional algorithms for association rule mining primarily rely on support and confidence metrics, which often fall short of addressing the complexities of real-time applications. High-utility itemset mining has emerged as a promising solution, offering significant insights by identifying items of substantial value to users. In this paper, we introduce a novel multi-utility range algorithm for computing high-utility itemsets. This algorithm is particularly advantageous for profit-oriented applications as it allows for the adjustment of utility ranges, thereby enabling the extraction of more valuable items. Our approach not only enhances the relevance of the mined itemsets but also provides flexibility in targeting different utility thresholds, making it highly effective for dynamic and real-time data analysis scenarios.
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