A database perspective on knowledge discovery
Communications of the ACM
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Geometric and combinatorial tiles in 0-1 data
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
Constraint relaxations for discovering unknown sequential patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Mining formal concepts with a bounded number of exceptions from transactional data
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
A bi-clustering framework for categorical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
From local pattern mining to relevant bi-cluster characterization
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Application-Independent Feature Construction from Noisy Samples
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Towards efficient mining of proportional fault-tolerant frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining bi-sets in numerical data
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Exploiting virtual patterns for automatically pruning the search space
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
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Thanks to an important research effort during the last few years, inductive queries on local patterns (e.g., set patterns) and their associated complete solvers have been proved extremely useful to support knowledge discovery. The more we use such queries on real-life data, e.g., biological data, the more we are convinced that inductive queries should return fault-tolerant patterns. This is obviously the case when considering formal concept discovery from noisy datasets. Therefore, we study various extensions of this kind of bi-set towards fault-tolerance. We compare three declarative specifications of fault-tolerant bi-sets by means of a constraint-based mining approach. Our framework enables a better understanding of the needed trade-off between extraction feasibility, completeness, relevance, and ease of interpretation of these fault-tolerant patterns. An original empirical evaluation on both synthetic and real-life medical data is given. It enables a comparison of the various proposals and it motivates further directions of research.