Trying to find balance between usability and speed for Formal Concept Analysis experiments.
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README.rst

formal-concepts

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Trying to find balance between usability and speed for Formal Concept Analysis experiments. Library is built on well known Python libraries Numpy (fast) and Pandas (easy to use). This library is work in progress, it is possible that whole implementation will change in the future.

What is Formal Concept Analysis?

Formal concept analysis (FCA) is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties. Each concept in the hierarchy represents the objects sharing some set of properties; and each sub-concept in the hierarchy represents a subset of the objects (as well as a superset of the properties) in the concepts above it. The term was introduced by Rudolf Wille in 1980, and builds on the mathematical theory of lattices and ordered sets that was developed by Garrett Birkhoff and others in the 1930s.

https://en.wikipedia.org/wiki/Formal_concept_analysis

Implemented algorithms

  • CloseByOne
  • FastCloseByOne
  • Lindig's UpperNeighbor

Benchmarks

Links

Dependencies

formal-concepts requires:

  • Python (>= 3.4)
  • NumPy
  • Pandas
  • pygraphviz (plotting)

Created 2018 by Tomáš Mikula