Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
ACM SIGKDD Explorations Newsletter
Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
Mining Constrained Association Rules to Predict Heart Disease
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
Mining patterns of dyspepsia symptoms across time points using constraint association rules
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
On interactive pattern mining from relational databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Constraint relaxations for discovering unknown sequential patterns
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
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
Semiring-Based Soft Constraints
Concurrency, Graphs and Models
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The paradigm of pattern discovery based on constraints has been recognized as a core technique in inductive querying: constraints provide 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. In our previous work [5], we analyzed such limitations and showed how they flow out from the same source: the fact that in the classical constraint-based mining, a constraint is a rigid boolean function which returns either true or false. To overcome such limitations we introduced the new paradigm of pattern discovery based on Soft Constraints, and instantiated our idea to the fuzzy soft constraints. In this paper we extend the framework to deal with probabilistic and weighted soft constraints: we provide theoretical basis and detailed experimental analysis. We also discuss a straightforward solution to deal with top-k queries. Finally we show how the ideas presented in this paper have been implemented in a real Inductive Database system.