Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Similarity and Fuzzy Tolerance Spaces
Journal of Logic and Computation
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
Embedding tolerance relations in formal concept analysis: an application in information fusion
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Towards fault-tolerant formal concept analysis
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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A numerical dataset is usually represented by a table where each entry denotes the value taken by an object in line for an attribute in column. A bicluster in a numerical data table is a subtable with close values different from values outside the subtable. Traditionally, largest biclusters were found by means of methods based on linear algebra. We propose an alternative approach based on concept lattices and lattices of interval pattern structures. In other words, this paper shows how formal concept analysis originally tackles the problem of biclustering and provides interesting perspectives of research.