Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
On mining closed sets in multi-relational data
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
A generic algorithm for generating closed sets of a binary relation
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Discovering Relevant Cross-Graph Cliques in Dynamic Networks
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Agglomerating local patterns hierarchically with ALPHA
Proceedings of the 18th ACM conference on Information and knowledge management
Mining triadic association rules from ternary relations
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
From triconcepts to triclusters
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Scalable mining of frequent tri-concepts from folksonomies
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Debugging embedded multimedia application traces through periodic pattern mining
Proceedings of the tenth ACM international conference on Embedded software
Incorporating occupancy into frequent pattern mining for high quality pattern recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
A quadratic approach for trend detection in folksonomies
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
A personalized recommender system based on users' information in folksonomies
Proceedings of the 22nd international conference on World Wide Web companion
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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Set pattern discovery from binary relations has been extensively studied during the last decade. In particular, many complete and efficient algorithms for frequent closed set mining are now available. Generalizing such a task to n-ary relations (n ≥ 2) appears as a timely challenge. It may be important for many applications, for example, when adding the time dimension to the popular objects × features binary case. The generality of the task (no assumption being made on the relation arity or on the size of its attribute domains) makes it computationally challenging. We introduce an algorithm called Data-Peeler. From an n-ary relation, it extracts all closed n-sets satisfying given piecewise (anti) monotonic constraints. This new class of constraints generalizes both monotonic and antimonotonic constraints. Considering the special case of ternary relations, Data-Peeler outperforms the state-of-the-art algorithms CubeMiner and Trias by orders of magnitude. These good performances must be granted to a new clever enumeration strategy allowing to efficiently enforce the closeness property. The relevance of the extracted closed n-sets is assessed on real-life 3-and 4-ary relations. Beyond natural 3-or 4-ary relations, expanding a relation with an additional attribute can help in enforcing rather abstract constraints such as the robustness with respect to binarization. Furthermore, a collection of closed n-sets is shown to be an excellent starting point to compute a tiling of the dataset.