EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data

Abstract

High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set of items with a utility higher or equivalent to a set benchmark. These itemsets are utilized to build association rules for data mining systems. However, in the age of big data, conventional (HUIM) strategies are least effective with limited processing capabilities. This work proposes an optimized technique, Enhanced Absolute High Utility Itemset Miner (EAHUIM) by incorporating various refinements into the Absolute High Utility Itemset Miner (AHUIM) Algorithm. EAHUIM discovers the itemsets from large datasets in near real-time and serves as the foundation for information management and decision-making systems by providing diverse insights. The experimental analysis reveals that EAHUIM outclasses other state-of-the-art algorithms for HUIM.

Keywords

• Big data
• Itemset mining
• Parallel computing
• Recommendation system
• Spark