Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relevant Attributes in Formal Contexts   Computing conceptual structures, like formal concept lattices, is in the age
of massive data sets a challenging task. There are various approaches to deal
with this, e.g., random sampling, parallelization, or attribute extraction. A
so far not investigated method in the realm of formal concept analysis is
attribute selection, as done in machine learning. Building up on this we
introduce a method for attribute selection in formal contexts. To this end, we
propose the notion of relevant attributes which enables us to define a relative
relevance function, reflecting both the order structure of the concept lattice
as well as distribution of objects on it. Finally, we overcome computational
challenges for computing the relative relevance through an approximation
approach based on information entropy.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: nlin
29
The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org