Association Rule Mining Bibliography

Journal articles

  1. D. Martín , A. Rosete , J. Alcalá-Fdez and F. Herrera. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. IEEE Transactions on Evolutionary Computation, Volume 18, Issue 1 (2014), Page 54-69.
  2. D. Martín , A. Rosete , J. Alcalá-Fdez and F. Herrera. QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules. Information Sciences, Volume 258 (2014), Page 1-28.
  3. J. Alcalá-Fdez , R. Alcalá and F. Herrera. A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems, Volume 19, Issue 5 (2011), Page 857-872.
  4. J. Alcalá-Fdez , N. Flugy-Pape , A. Bonarini and F. Herrera. Analysis of the effectiveness of the genetic algorithms based on extraction of association rules. Fundamenta Informaticae, Volume 98, Issue 1 (2010), Page 1-14.
  5. J. Alcalá-Fdez , R. Alcalá , M.J. Gacto and F. Herrera. Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets and Systems, Volume 160, Issue 7 (2009), Page 905-921.

Conference contributions

  1. F. Charte , A. Rivera , M.J. Del Jesus and F. Herrera Improving Multi-label Classifiers via Label Reduction with Association Rules, Proceedings of the 7th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2012, pag: 188-199, ISBN: 978-3-642-28930-9.
  2. D. Martín , A. Rosete , J. Alcalá-Fdez and F. Herrera. A multi-objective evolutionary algorithm for mining quantitative association rules, Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, ISDA 2011, pag: 1397-1402, ISBN: 978-1-4577-1676-8.
  3. M. Fazzorali , R. Alcalá , Y. Nojima , H. Ishibuchi and F. Herrera. Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities, Proceedings of the 2013 International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS 2013, pag: 44-51, ISBN: 978-146735899-6.
  4. J. Alcalá-Fdez , R. Alcalá and F. Herrera. A fuzzy associative classification system with genetic rule selection for high-dimensional problems, Proceedings of the 4th International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS 2010, pag: 33-38, ISBN: 978-1-4244-4621-6.
  5. N. Flugy-Pape , J. Alcalá-Fdez , A. Bonarini and F. Herrera Evolutionary extraction of association rules: A preliminary study on their effectiveness, Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009, pag: 646-653, ISBN: 978-3-642-02318-7.
  6. R. Alcalá , J. Alcalá-Fdez , M.J. Gacto and F. Herrera. Genetic learning of membership functions for mining fuzzy association rules, Proceedings of the IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007, pag: 1-6, ISBN: 1-4244-1209-9.