Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Formalizing Hypotheses with Concepts
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
The Use of Associative Concepts in the Incremental Building of a Logical Context
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Discovering attribute relationships, dependencies and rules by using rough sets
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Galois Connections and Data Analysis
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2003)
Learning closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Non-symmetric indiscernibility
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Multi-adjoint fuzzy rough sets: Definition, properties and attribute selection
International Journal of Approximate Reasoning
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We propose a unifying FCA-based framework for some questions in data analysis and data mining, combining ideas from Rough Set Theory, JSM-reasoning, and feature selection in machine learning. Unlike the standard rough set model the indiscernibility relation in our paper is based on a quasi-order, not necessarily an equivalence relation. Feature selection, though algorithmically difficult in general, appears to be easier in many cases of scaled many-valued contexts, because the difficulties can at least partially be projected to the scale contexts. We propose a heuristic algorithm for this.