High-Utility Association Rules Mining Based-on Binary Particle Swarm Optimization
- 10.2991/aer.k.211129.016How to use a DOI?
- high-utility association rules mining; binary particle swarm optimization; BPSO approach
Traditional association rule mining algorithm only generates a set of rules from frequent itemset, the rules obtained cannot generate rules from high-utility itemset. This is because the framework that’s being used to obtain rules from traditional association rule is support-confidence while getting high-utility itemset association rules uses the utility-confidence framework. The model for high-utility association rule mining proposed is using particle swarm optimization. The fitness function to get high-utility association rules does not use support-confidence but uses the utility-confidence framework. The association rule mining model of high-utility itemset does not look for high-utility itemset first but together with the high-utility itemset mining process. The high-utility association rule mining using the particle swarm optimization approach has better rule set quality than using the traditional approach, Apriori. Testing with five datasets: chess, connect, mushroom, accident, and foodmart, shows the average utility-confidence obtained using our proposed method is above 88%.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - R. Gunawan AU - E. Winarko AU - R. Pulungan PY - 2021 DA - 2021/11/30 TI - High-Utility Association Rules Mining Based-on Binary Particle Swarm Optimization BT - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020) PB - Atlantis Press SP - 71 EP - 74 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.211129.016 DO - 10.2991/aer.k.211129.016 ID - Gunawan2021 ER -