Efficient top-down induction of logic programs
ACM SIGART Bulletin
Efficient mining of association rules using closed itemset lattices
Information Systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
IEEE Intelligent Systems
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
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning communicative actions of conflicting human agents
Journal of Experimental & Theoretical Artificial Intelligence
Pattern Structures for Analyzing Complex Data
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Scale coarsening as feature selection
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Concept-based learning of human behavior for customer relationship management
Information Sciences: an International Journal
Computing graph-based lattices from smallest projections
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Semi-supervised learning for mixed-type data via formal concept analysis
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
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
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
Mining closed patterns in relational, graph and network data
Annals of Mathematics and Artificial Intelligence
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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Similarity of graphs with labeled vertices and edges is naturally defined in terms of maximal common subgraphs. To avoid computation overload, a parameterized technique for approximation of graphs and their similarity is used. A lattice-based method of binarizing labeled graphs that respects the similarity operation on graph sets is proposed. This method allows one to compute graph similarity by means of algorithms for computing closed sets. Results of several computer experiments in predicting biological activity of chemical compounds that employ the proposed technique testify in favour of graph approximations as compared to complete graph representations: gaining in efficiency one (almost) does not lose in accuracy.