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- W2307901741 abstract "In this paper, we developed a Binary Particle Swarm Optimization (BPSO) based fuzzy association rule miner to generate fuzzy association rules from a transactional database by formulating a combinatorial global optimization problem, without pre-defining minimum support and confidence unlike other conventional association miners. Goodness of fuzzy association rules is measured by a fitness function viz., the product of support and confidence. So as to demonstrate the effectiveness of our method, we implemented it to phishing detection domain. Based on the goodness of the rules obtained, we infer that our proposed algorithm can be used as a sound alternative to the fuzzy apriori algorithm." @default.
- W2307901741 created "2016-06-24" @default.
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- W2307901741 date "2015-12-01" @default.
- W2307901741 modified "2023-09-25" @default.
- W2307901741 title "Fuzzy association rule mining using binary particle swarm optimization: Application to cyber fraud analytics" @default.
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- W2307901741 doi "https://doi.org/10.1109/iccic.2015.7435765" @default.
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