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Galois Connections between Semimodules and Applications in Data Mining
In [1] a generalisation of Formal Concept Analysis was introduced
with data mining applications in mind, K-Formal Concept Analysis,
where incidences take values in certain kinds of semirings, instead
of the standard Boolean carrier set. A fundamental result was missing
there, namely the second half of the equivalent of the main theorem of
Formal Concept Analysis. In this continuation we introduce the structural
lattice of such generalised contexts, providing a limited equivalent
to the main theorem of K-Formal Concept Analysis which allows to interpret
the standard version as a privileged case in yet another direction.
We motivate our results by providing instances of their use to analyse
the confusion matrices of multiple-input multiple-output classifiers
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