Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Using a hash-based method for apriori-based graph mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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 closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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From the mathematical perspective, lattices of closed descriptions, which arise often in practical applications can be reduced to concept lattices by means of the Basic Theorem of Formal Concept Analysis (FCA). From the computational perspective, in many cases it is more advantageous to process closed descriptions and their lattices directly, without reducing them to concept lattices. Here a method for computing lattices with descriptions given by sets of graphs, starting with rough approximations is considered and compared to previous approaches.