أركان أ. غيب، يحيى عنيد عبد الرضا الصالحي، إسراء م. حيدر، حسين أ. يونس، عبد الله أ. ناهي
English Authors
Arkan A. Ghaib, Yahya Eneid Abdulridha Alsalhi, Israa M. Hayder, Hussain A. Younis and Abdullah A. Nahi
Abstract
High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility
values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and
benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While
several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large
datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility
itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM)
algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights
as the basis for information management and nearly real-time decision-making systems. According to experimental
investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The
results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.
Publication Date
11/24/2023
Publisher
Journal of Computer Science and Technology Studies
حسين عبد الكريم يونس | hussain A.younis | 5874