P. Bohannon, W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis, Conditional Functional Dependencies for Data Cleaning, 2007 IEEE 23rd International Conference on Data Engineering, pp.746-755, 2007.
DOI : 10.1109/ICDE.2007.367920

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.259.8695

L. Berti-equille, T. Dasu, and D. Srivastava, Discovery of complex glitch patterns: A novel approach to Quantitative Data Cleaning, 2011 IEEE 27th International Conference on Data Engineering, 2011.
DOI : 10.1109/ICDE.2011.5767864

B. Goethals, S. Moens, and J. Vreeken, MIME, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.757-760, 2011.
DOI : 10.1145/2020408.2020529

Y. Huhtala, J. Karkkainen, P. Porkka, and H. Toivonen, Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies, The Computer Journal, vol.42, issue.2, pp.100-111, 1999.
DOI : 10.1093/comjnl/42.2.100

C. Hurter, A. Telea, and . Ersoyo, MoleView: An Attribute and Structure-Based Semantic Lens for Large Element-Based Plots, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.12, pp.2600-2609, 2011.
DOI : 10.1109/TVCG.2011.223

URL : https://hal.archives-ouvertes.fr/hal-01021608

N. Novelli and R. Cicchetti, Functional and embedded dependency inference: a data mining point of view, Information Systems (IS), pp.477-506, 2001.
DOI : 10.1016/S0306-4379(01)00032-1