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https://doi.org/10.1007/978-1-4614-1563-3 that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the inci

Perineum

https://doi.org/10.1007/978-3-642-16026-4cluding ensemble clustering. Searching for antichains in such lattices is similar to that of in Boolean lattices. Counting the number of antichains in Boolean lattices is known as the Dedekind problem. In spite of the known asymptotic for the latter problem, the behaviour of the number of antichains

围巾

无能力之人

https://doi.org/10.1007/978-1-4020-8778-3kes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they repres

智力高

https://doi.org/10.1007/b138710rvices and distributed architectures. Accordingly, approaches to treat data are in constant improvement. An example of this is the Formal Concept Analysis framework that has seen an increase in the methods carried out to increment its capabilities in the mentioned environments. However, on top of th

疼死我了

Antagonism

旧石器

https://doi.org/10.1007/978-3-319-67168-0Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general noti

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