Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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 emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Divide-and-Approximate: A Novel Constraint Push Strategy for Iceberg Cube Mining
IEEE Transactions on Knowledge and Data Engineering
Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Optimizing Constraint-Based Mining by Automatically Relaxing Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Constraint relaxations for discovering unknown sequential patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Database transposition for constrained (closed) pattern mining
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Interestingness is not a dichotomy: introducing softness in constrained pattern mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Exploiting virtual patterns for automatically pruning the search space
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Discovering Emerging Graph Patterns from Chemicals
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Extracting and summarizing the frequent emerging graph patterns from a dataset of graphs
Journal of Intelligent Information Systems
Incorporating occupancy into frequent pattern mining for high quality pattern recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
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Constraint-based mining is an active field of research which is a necessary step to achieve interactive and successful KDD processes. The limitations of the task lies in languages being limited to describe the mined patterns and the ability to express varied constraints. In practice, current approaches focus on a language and the most generic frameworks mine individually or simultaneously a monotone and an anti-monotone constraints. In this paper, we propose a generic framework dealing with any partially ordered language and a large set of constraints. We prove that this set of constraints called primitive-based constraints not only is a superclass of both kinds of monotone ones and their boolean combinations but also other classes such as convertible and succinct constraints. We show that the primitive-based constraints can be efficiently mined thanks to a relaxation method based on virtual patterns which summarize the specificities of the search space. Indeed, this approach automatically deduces pruning conditions having suitable monotone properties and thus these conditions can be pushed into usual constraint mining algorithms. We study the optimal relaxations. Finally, we provide an experimental illustration of the efficiency of our proposal by experimenting it on several contexts.