Exploring Faulty Data
Within formal concept analysis, attribute exploration is a powerful tool to semi-automatically check data for completeness with respect to a given domain. However, the classical formulation of attribute exploration does not take into account possible errors which are present in the initial data. To remedy this, we present in this work a generalization of attribute exploration based on the notion of confidence, that will allow for the exploration of implications which are not necessarily valid in the initial data, but instead enjoy a minimal confidence therein.
This work has been partially supported by the DFG Research Training Group 1763 “QuantLA”, and by the Cluster of Excellence “Center for Advancing Electronics Dresden” (cfAED). Additionally, the author is grateful to the anonymous reviewers for the detailed and helpful comments.
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