Efficient mining of association rules using closed itemset lattices
Information Systems
A fast algorithm for building lattices
Information Processing Letters
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
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Using transposition for pattern discovery from microarray data
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Finding Top-N Pseudo Formal Concepts with Core Intents
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Supporting bi-cluster interpretation in 0/1 data by means of local patterns
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Application of discrimination degree for attributes reduction in concept lattice
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient polynomial delay algorithm for pseudo frequent itemset mining
DS'07 Proceedings of the 10th international conference on Discovery science
Ambiguous frequent itemset mining and polynomial delay enumeration
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Two measures of objective novelty in association rule mining
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Towards fault-tolerant formal concept analysis
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
From local pattern mining to relevant bi-cluster characterization
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Mining a new fault-tolerant pattern type as an alternative to formal concept discovery
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
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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We are designing new data mining techniques on boolean contexts to identify a priori interesting bi-sets (i.e., sets of objects or transactions associated to sets of attributes or items). A typical important case concerns formal concept mining (i.e., maximal rectangles of true values or associated closed sets by means of the so-called Galois connection). It has been applied with some success to, e.g., gene expression data analysis where objects denote biological situations and attributes denote gene expression properties. However in such real-life application domains, it turns out that the Galois association is a too strong one when considering intrinsically noisy data. It is clear that strong associations that would however accept a bounded number of exceptions would be extremely useful. We study the new pattern domain of α/β concepts, i.e., consistent maximal bi-sets with less than α false values per row and less than β false values per column. We provide a complete algorithm that computes all the α/β concepts based on the generation of concept unions pruned thanks to anti-monotonic constraints. An experimental validation on synthetic data is given. It illustrates that more relevant associations can be discovered in noisy data. We also discuss a practical application in molecular biology that illustrates an incomplete but quite useful extraction when all the concepts that are needed beforehand can not be discovered.