Attribute exploration with background knowledge
Theoretical Computer Science
Understanding class hierarchies using concept analysis
ACM Transactions on Programming Languages and Systems (TOPLAS)
Fuzzy Relational Systems: Foundations and Principles
Fuzzy Relational Systems: Foundations and Principles
Using a Concept Lattice of Decomposition Slices for Program Understanding and Impact Analysis
IEEE Transactions on Software Engineering
Visualizing class interfaces with formal concept analysis
OOPSLA '03 Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Refactoring class hierarchies with KABA
OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Theory of Relational Databases
Theory of Relational Databases
Mining Security-Sensitive Operations in Legacy Code Using Concept Analysis
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Finding Conceptual Document Clusters with Improved Top-N Formal Concept Search
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Formal concept analysis with background knowledge: attribute priorities
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Isotone fuzzy Galois connections with hedges
Information Sciences: an International Journal
Evaluation of IPAQ questionnaires supported by formal concept analysis
Information Sciences: an International Journal
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift
IEEE Computational Intelligence Magazine
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We propose a method to control the structure of concept lattices derived from Boolean data. Concept lattices represent the basic structure utilized in formal concept analysis. Their structure is of primary importance for the analysis and understanding of the input data. Our method enables to control the structure of the derived concept lattice by specifying granularity levels of attributes, thus in a sense by focusing the lenses through which we perceive and conceptually carve up the world. The granularity levels are chosen by a user based on his expertise and experimentation with the data. If the resulting formal concepts are too specific and there is a large number of them, the user can choose to use a coarser level of granularity. The resulting formal concepts are then less specific and can be seen as resulting from a zoom-out. In a similar way, one may perform a zoom-in to obtain finer, more specific formal concepts. The paper presents a basic study of this topic. We describe the motivations, the method, a theoretical insight, zoom-in and zoom-out algorithms, and experiments demonstrating the method.