Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy Logic Technology and Applications I
Fuzzy Logic Technology and Applications I
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Relational Systems: Foundations and Principles
Fuzzy Relational Systems: Foundations and Principles
Efficient Data Mining Based on Formal Concept Analysis
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Applying hybrid reasoning to mine for associative features in biological data
Journal of Biomedical Informatics
Discovering shared conceptualizations in folksonomies
Web Semantics: Science, Services and Agents on the World Wide Web
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Formally analysing the concepts of domestic violence
Expert Systems with Applications: An International Journal
Optimal decompositions of matrices with grades into binary and graded matrices
Annals of Mathematics and Artificial Intelligence
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
A formal concept analysis approach to rough data tables
Transactions on rough sets XIV
Formal concept analysis constrained by attribute-dependency formulas
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Measuring Inconsistency in Fuzzy Answer Set Semantics
IEEE Transactions on Fuzzy Systems
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Concept lattices of incomplete data
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Modeling preferences over attribute sets in formal concept analysis
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
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We propose a new method for modelling users' preferences on attributes that contain more than one trait. Starting with a data set the users have to enter a sort of order on the attributes in form of formulas corresponding to their preferences. Based on this order they only receive the relevant formal concepts, i.e., ''object-attribute clusters'', where relevant corresponds to the users' point of view. The preference modelling is done within the framework of Formal Fuzzy Concept Analysis. This has numerous advantages. First, the relevant information is contained in a complete lattice, the concept lattice, that allows the users to browse among their preferences. This lattice may be used for further data analysis by applying different methods from Formal Concept Analysis. Second, we can investigate the computation of non-redundant bases for the entered formulas. Since the users are allowed to enter the formulas, these may be redundant. The base offers a better overview of the preferences and thus the formulas can be altered more easily.