Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)

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

Authors
R. Gunawan*
Department of Informatics, Faculty of Science and Technology, Sanata Dharma University, Yogyakarta, Indonesia
E. Winarko, R. Pulungan
Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
Corresponding Author
R. Gunawan
Available Online 30 November 2021.
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.

Download article (PDF)

Volume Title
Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
Series
Advances in Engineering Research
Publication Date
30 November 2021
ISBN
978-94-6239-472-8
ISSN
2352-5401
DOI
10.2991/aer.k.211129.016How to use a DOI?
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.

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  -