Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
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
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Efficient Local Search in Conceptual Clustering
DS '01 Proceedings of the 4th International Conference on Discovery Science
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A context model for fuzzy concept analysis based upon modal logic
Information Sciences—Informatics and Computer Science: An International Journal
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
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
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
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Gene expression array exploration using K-formal concept analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
Biclustering numerical data in formal concept analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
A game theoretic framework for heterogenous information network clustering
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate bicluster and tricluster boxes in the analysis of binary data
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
In-close2, a high performance formal concept miner
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
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
A conceptual approach to gene expression analysis enhanced by visual analytics
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Discovering Knowledge in Data Using Formal Concept Analysis
International Journal of Distributed Systems and Technologies
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Given Boolean data sets which record properties of objects, Formal Concept Analysis is a well-known approach for knowledge discovery. Recent application domains, e.g., for very large data sets, have motivated new algorithms which can perform constraint-based mining of formal concepts (i.e., closed sets on both dimensions which are associated by the Galois connection and satisfy some user-defined constraints). In this paper, we consider a major limit of these approaches when considering noisy data sets. This is indeed the case of Boolean gene expression data analysis where objects denote biological experiments and attributes denote gene expression properties. In this type of intrinsically noisy data, the Galois association is so strong that the number of extracted formal concepts explodes. We formalize the computation of the so-called δ-bi-sets as an alternative for capturing strong associations between sets of objects and sets of properties. Based on a previous work on approximate condensed representations of frequent sets by means of δ-free itemsets, we get an efficient technique which can be applied on large data sets. An experimental validation on both synthetic and real data is given. It confirms the added-value of our approach w.r.t. formal concept discovery, i.e., the extraction of smaller collections of relevant associations.