Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Adequate condensed representations of patterns
Data Mining and Knowledge Discovery
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
ORIGAMI: Mining Representative Orthogonal Graph Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
One in a million: picking the right patterns
Knowledge and Information Systems
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Fast extraction of locally optimal patterns based on consistent pattern function variations
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
GRAONTO: A graph-based approach for automatic construction of domain ontology
Expert Systems with Applications: An International Journal
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This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. Finally the application of MIPs to labelled graphs, here molecular graphs, is discussed.