Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A new imputation method for small software project data sets
Journal of Systems and Software
Attribute-incremental construction of the canonical implication basis
Annals of Mathematics and Artificial Intelligence
Some decision and counting problems of the Duquenne-Guigues basis of implications
Discrete Applied Mathematics
Reasoning with characteristic models
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Towards certain fixes with editing rules and master data
Proceedings of the VLDB Endowment
On the complexity of enumerating pseudo-intents
Discrete Applied Mathematics
Counting pseudo-intents and #p-completeness
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
Computing premises of a minimal cover of functional dependencies is intractable
Discrete Applied Mathematics
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We suggest a classification of possible mistakes in lines of binary data tables (in formal contexts) and discuss their detection. An approach is proposed to detect some types of mistakes in new lines (contents of objects) of binary data tables (formal contexts). This approach is based on detecting those implications from implication bases of the formal contexts that are not met by a new object. It is noted that this approach can result in a complex computational solution. An alternative approach based on computing closures of subsets of object's intent. This approach allows one to find a polynomial algorithm for the solution. The algorithm of unnecessary and extra missing properties can also be used. The results of experiments are dealt with.