High-Utility Association Rules Mining Based-on Binary Particle Swarm Optimization

DOI

10.2991/aer.k.211129.016How to use a DOI?

Keywords

high-utility association rules mining; binary particle swarm optimization; BPSO approach

Abstract

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%.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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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  -