Efficient mining of association rules using closed itemset lattices
Information Systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generalized Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Formalizing Hypotheses with Concepts
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Learning of Simple Conceptual Graphs from Positive and Negative Examples
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Two FCA-Based Methods for Mining Gene Expression Data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
Learning closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
A possibility theory-oriented discussion of conceptual pattern structures
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Symbolic galois lattices with pattern structures
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Some remarks on the relation between annotated ordered sets and pattern structures
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Why and how knowledge discovery can be useful for solving problems with CBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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For data given by binary object-attribute datatables Formal Concept Analysis (FCA) provides with a means for both convenient computing hierarchies of object classes and dependencies between sets of attributes used for describing objects. In case of data more complex than binary to apply FCA techniques, one needs scaling (binarizing) data. Pattern structures propose a direct way of processing complex data such as strings, graphs, numerical intervals and other. As compared to scaling (binarization), this way is more efficient from the computational point of view and proposes much better vizualization of results. General definition of pattern structures and learning by means of them is given. Two particular cases, namely that of graph pattern structures and interval pattern structures are considered. Applications of these pattern structures in bioinformatics are discussed.