Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A Logical Generalization of Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Generalized Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A Possibility-Theoretic View of Formal Concept Analysis
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Two FCA-Based Methods for Mining Gene Expression Data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Many-Valued Concept Lattices for Conceptual Clustering and Information Retrieval
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Interval data and nested lattices
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Symbolic galois lattices with pattern structures
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
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
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This paper holds on the application of two classification methods based on formal concept analysis (FCA) to interval data. The first method uses a similarity between objects while the second considers so-called pattern structures. We deeply detail these methods in order to show their close links. This parallel study helps understanding complex data with concept lattices. We explain how the second method obtains same results and how to handle missing values. Most importantly, this is achieved in full compliance with the FCA-framework, and thus benefits from existing and efficient tools such as algorithms. Finally, an experiment on real-world data in agronomy has been carried out for decision helping in agricultural practices.