Reduction in Triadic Data Sets

Sebastian RudolphSebastian Rudolph, Christian SăcăreaChristian Săcărea, Diana TroancăDiana Troancă

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Sebastian Rudolph, Christian Săcărea, Diana Troancă
Reduction in Triadic Data Sets
Workshop FCA4AI „What cand FCA do for Artificial Intelligence?”, pp.55-62, July 2015

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KurzfassungAbstract
Even if not explicitly stated, data can be often interpreted in a triadic setting in numerous scenarios of data analysis and processing. Formal Concept Analysis, as the underlying mathematical theory of Conceptual Knowledge Processing gives the possibility to explore the structure of data and to understand its structure. Representing knowledge as conceptual hierarchies becomes increasingly popular as a basis for further communication of knowledge. While in the dyadic setting there are well-known methods to reduce the complexity of data without affecting its underlying structure, these methods are missing in the triadic case. Driven by practical requirements, we discuss an extension of the classical reduction methods to the triadic case and apply them to a medium-sized oncological data set.
Forschungsgruppe:Research Group: Computational LogicComputational Logic

@inproceedings{RST2015,
  author    = {Sebastian Rudolph and Christian S{\u{a}}c{\u{a}}rea and Diana
               Troanc{\u{a}}},
  title     = {Reduction in Triadic Data Sets},
  booktitle = {Workshop {FCA4AI} {,,}What cand {FCA} do for Artificial
               Intelligence?{\textquotedblright}},
  year      = {2015},
  month     = {July},
  pages     = {pp.55-62}
}