Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of 20th International Conference on Very Large Data Bases, pp.487-499, 1994. ,
, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2004.
Information Dependencies, Proceedings of the 19th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp.245-253, 2000. ,
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Min. Knowl. Discov, vol.8, issue.1, pp.53-87, 2004. ,
Finding low-entropy sets and trees from binary data, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, p.350, 2007. ,
Low-Entropy Set Selection, Proceedings of the SIAM International Conference on Data Mining, pp.569-580, 2009. ,
Maximally informative k-itemsets and their efficient discovery, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.237-244, 2006. ,
Mining Non-redundant Information-Theoretic Dependencies between Itemsets, Proceedings of the 12th international conference on Data warehousing and knowledge discovery, pp.130-141, 2010. ,
Discovering Reliable Approximate Functional Dependencies, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.355-363, 2017. ,
Fast Extraction of Locally Optimal Patterns Based on Consistent Pattern Function Variations, Machine Learning and Knowledge Discovery in Databases, European Conference, Proceedings, vol.6323, pp.34-49, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00552893
The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases, Machine Learning and Knowledge Discovery in Databases, European Conference, Proceedings, vol.5782, pp.205-220, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00437536
Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance, Proceedings of the 31st International Conference on Machine Learning, vol.32, pp.1143-1151, 2014. ,
A Framework to Adjust Dependency Measure Estimates for Chance, Proceedings of the 2016 SIAM International Conference on Data Mining, pp.423-431, 2016. ,
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance, J. Mach. Learn. Res, vol.11, pp.2837-2854, 2010. ,