Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Lattice of Concept Graphs of a Relationally Scaled Context
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Mining Graph Data
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
The Description Logic Handbook
The Description Logic Handbook
Relational concept discovery in structured datasets
Annals of Mathematics and Artificial Intelligence
Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Toward Recommendation Based on Ontology-Powered Web-Usage Mining
IEEE Internet Computing
Concept learning in description logics using refinement operators
Machine Learning
A proposal for combining formal concept analysis and description logics for mining relational data
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Refactorings of design defects using relational concept analysis
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Supporting ontology design through large-scale FCA-based ontology restructuring
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Selection of Composable Web Services Driven by User Requirements
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Building abstractions in class models: formal concept analysis in a model-driven approach
MoDELS'06 Proceedings of the 9th international conference on Model Driven Engineering Languages and Systems
Arbitrary relations in formal concept analysis and logical information systems
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
Generation of operational transformation rules from examples of model transformations
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Incrementally building frequent closed itemset lattice
Expert Systems with Applications: An International Journal
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The processing of complex data is admittedly among the major concerns of knowledge discovery from data (kdd). Indeed, a major part of the data worth analyzing is stored in relational databases and, since recently, on the Web of Data. This clearly underscores the need for Entity-Relationship and rdf compliant data mining (dm) tools. We are studying an approach to the underlying multi-relational data mining (mrdm) problem, which relies on formal concept analysis (fca) as a framework for clustering and classification. Our relational concept analysis (rca) extends fca to the processing of multi-relational datasets, i.e., with multiple sorts of individuals, each provided with its own set of attributes, and relationships among those. Given such a dataset, rca constructs a set of concept lattices, one per object sort, through an iterative analysis process that is bound towards a fixed-point. In doing that, it abstracts the links between objects into attributes akin to role restrictions from description logics (dls). We address here key aspects of the iterative calculation such as evolution in data description along the iterations and process termination. We describe implementations of rca and list applications to problems from software and knowledge engineering.