R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, 20th International Conference on Very Large Data Bases, (VLDB'94), pp.487-499, 1994.

F. Berzal, J. Cubero, D. Sanchez, M. Vila, and J. Serrano, An alternative approach to discover gradual dependencies, Fuzziness and Knowledge-Based Systems (IJUFKS), vol.15, pp.559-570, 2007.

P. Bosc, O. Pivert, and L. Ughetto, On data summaries based on gradual rules, Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications, pp.512-521, 1999.

D. Dubois and H. Prade, Gradual inference rules in approximate reasoning, Inf. Sci, vol.61, issue.1-2, pp.103-122, 1992.

D. Dubois and H. Prade, Gradual elements in a fuzzy set, Soft Comput, vol.12, issue.2, pp.165-175, 2008.

C. Fiot, F. Masseglia, A. Laurent, and M. Teisseire, Gradual trends in fuzzy sequential patterns, 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, 2008.
URL : https://hal.archives-ouvertes.fr/lirmm-00273910

E. Hüllermeier, Association rules for expressing gradual dependencies, PKDD '02: Proceedings of the 6th

, European Conference on Principles of Data Mining and Knowledge Discovery, pp.200-211, 2002.

H. Jones, D. Dubois, S. Guillaume, and B. Charnomordic, A practical inference method with several implicative gradual rules and a fuzzy input: one and two dimensions. Fuzzy Systems Conference, 2007. FUZZ-IEEE, IEEE International, pp.1-6, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00448927

F. Masseglia, P. Poncelet, and M. Teisseire, Pre-processing time constraints for efficiently mining generalized sequential patterns, 11th International Symposium on Temporal Representation and Reasoning (TIME '04), pp.87-95, 2004.
URL : https://hal.archives-ouvertes.fr/lirmm-00108888