Research Article

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Authors

  • Arkan A. Ghaib Department of Information Technology, Management Technical College, Southern Technical University, Basrah, Iraq
  • Yahya Eneid Abdulridha Alsalhi General Directorate of Education Dhi Qar, Ministry of Education, Baghdad, Iraq
  • Israa M. Hayder Dept. Of Computer Systems Techniques, Qurna Technique Institute, STU, Basrah, Iraq
  • Hussain A. Younis College of Education for Women, University of Basrah, Iraq, School of Computer Sciences, University. Sains Malaysia, 11800 USM, Penang, Malaysia
  • Abdullah A. Nahi Department of Information Technology, Management Technical College, Southern Technical University, Basrah, Iraq

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.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

5 (4)

Pages

122-131

Published

2023-11-24

How to Cite

Ghaib, A. A., Alsalhi, Y. E. A., Hayder, I. M., Younis, H. A., & Nahi, A. A. (2023). Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data. Journal of Computer Science and Technology Studies, 5(4), 122–131. https://doi.org/10.32996/jcsts.2023.5.4.12

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Keywords:

High utility item sets, Item sets mining, High utility pattern, Parallel computing. Big data