Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Soft constraint based pattern mining
Data & Knowledge Engineering
A constraint-based querying system for exploratory pattern discovery
Information Systems
Mining constraint-based patterns using automatic relaxation
Intelligent Data Analysis
Extending the soft constraint based mining paradigm
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Inductive databases and constraint-based data mining
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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The paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. As a solution, in this paper we introduce the new paradigm of pattern discovery based on Soft Constraints. Albeit simple, the proposed paradigm overcomes all the major methodological drawbacks of the classical constraint-based paradigm, representing an important step further towards practical pattern discovery.