Probabilistic generalization of formal concepts

Authors:
E. E. Vityaev;A. V. Demin;D. K. Ponomaryov
Affiliations:
S.L. Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 6300090;A.P. Ershov Institute of Information Systems, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 630090;A.P. Ershov Institute of Information Systems, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 630090
Venue:
Programming and Computing Software
Year:
2012

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Abstract

An inductive probabilistic approach to formal concept analysis (FCA) is proposed in which probability on formal contexts is considered; probabilistic formal concepts that have predictive force are defined: nonclassified objects can be assigned to earlier found probabilistic formal concepts; random attributes are eliminated from probabilistic formal concepts; probabilistic formal concepts are robust with respect to data noise. A result of experiment is presented in which formal concepts (in their standard definition in FCA) are first distorted by random noise and then recovered by detecting probabilistic formal concepts.