TY - GEN
T1 - Discovering dependencies among mined association rules with population-based metaheuristics
AU - Fister, Iztok
AU - Del Ser, Javier
AU - Galvez, Akemi
AU - Iglesias, Andres
AU - Osaba, Eneko
AU - Fister, Iztok
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/13
Y1 - 2019/7/13
N2 - Stochastic population-based nature-inspired metaheuristics have been proven as a robust tool for mining association rules. These algorithms are very scalable, as well as very fast compared with some deterministic ones that search for solutions exhaustively. Typically, algorithms for association rule mining identify a lot of rules depending, on the transaction database and number of attributes. Therefore, evaluating these rules is very complex. On the other hand, establishing the relationships between discovered association rules can be considered as a very hard problem that cannot easily be solved manually. In this paper, we propose a new algorithm based on stochastic population-based nature-inspired metaheuristics for discovering dependencies among association rules.
AB - Stochastic population-based nature-inspired metaheuristics have been proven as a robust tool for mining association rules. These algorithms are very scalable, as well as very fast compared with some deterministic ones that search for solutions exhaustively. Typically, algorithms for association rule mining identify a lot of rules depending, on the transaction database and number of attributes. Therefore, evaluating these rules is very complex. On the other hand, establishing the relationships between discovered association rules can be considered as a very hard problem that cannot easily be solved manually. In this paper, we propose a new algorithm based on stochastic population-based nature-inspired metaheuristics for discovering dependencies among association rules.
KW - Association rule mining
KW - Complex networks
KW - Graphs
KW - Population-based metaheuristics
UR - http://www.scopus.com/inward/record.url?scp=85070627139&partnerID=8YFLogxK
U2 - 10.1145/3319619.3326833
DO - 10.1145/3319619.3326833
M3 - Conference contribution
AN - SCOPUS:85070627139
T3 - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
SP - 1668
EP - 1674
BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Y2 - 13 July 2019 through 17 July 2019
ER -