Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Conceptual on-line analytical processing
Information organization and databases
Relational Scaling and Databases
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
TOSCANA - a Graphical Tool for Analyzing and Exploring Data
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
OntoComP: A Protégé Plugin for Completing OWL Ontologies
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Formal concept analysis in information science
Annual Review of Information Science and Technology
From formal concept analysis to contextual logic
Formal Concept Analysis
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
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Business Intelligence solutions provide different means like OLAP, data mining or case based reasoning to explore data. Standard BI means are usually based on mathematical statistics and provide a quantitative analysis of the data. In this paper, a qualitative approach based on a mathematical theory called "Formal Concept Analysis" (FCA) is used instead. FCA allows clustering a given set of objects along attributes acting on the objects, hierarchically ordering those clusters, and finally visualizing the cluster hierarchy in so-called Hasse-diagrams. The approach in this paper is exemplified on a dataset of documents crawled from the SAP community network, which are persisted in a semantic triple store and evaluated with an existing FCA tool called "ToscanaJ" which has been modified in order to retrieve its data from a triple store.