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
Conceptual Knowledge Discovery and Data Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on 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
Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
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
Towards fault-tolerant formal concept analysis
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
What Can Formal Concept Analysis Do for Data Warehouses?
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
A case study on financial ratios via cross-graph quasi-bicliques
Information Sciences: an International Journal
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Constraint programming for mining n-ary patterns
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
From triconcepts to triclusters
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
MFCluster: mining maximal fault-tolerant constant row biclusters in microarray dataset
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Mining fault-tolerant item sets using subset size occurrence distributions
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Finding ensembles of neurons in spike trains by non-linear mapping and statistical testing
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Combining CSP and constraint-based mining for pattern discovery
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A knowledge-driven bi-clustering method for mining noisy datasets
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
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Formal concept analysis has been proved to be useful to support knowledge discovery from boolean matrices. In many applications, such 0/1 data have to be computed from experimental data and it is common to miss some one values. Therefore, we extend formal concepts towards fault-tolerance. We define the DR-bi-set pattern domain by allowing some zero values to be inside the pattern. Crucial properties of formal concepts are preserved (number of zero values bounded on objects and attributes, maximality and availability of functions which “connect” the set components). DR-bi-sets are defined by constraints which are actively used by our correct and complete algorithm. Experimentation on both synthetic and real data validates the added-value of the DR-bi-sets.