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| Title | : | High-Utility Association Rules Mining Based-on Binary Particle Swarm Optimization |
| Author | : |
RIDOWATI GUNAWAN (1) Drs. Edi Winarko, M.Sc.,Ph.D. (2) Prof. Dr.-Ing. Mhd. Reza M. I. Pulungan, S.Si., M.Sc. (3) |
| Date | : | 0 2020 |
| Keyword | : | high-utility association rules mining,binary particle swarm optimization,BPSO approach 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%. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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2-2-High-Utility Association Rules Mining Based-on Binary Particle Swarm Optimization.pdf
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