Paper: Sebastian Rudolph, Christian Săcărea, Diana Troancă, Reduction in Triadic Data Sets, International Workshop “What can FCA do for Artificial Intelligence?” FCA4AI International Joint Conference on Artificial Intelligence IJCAI 2015 July 25, 2015 Buenos Aires, Argentina pp.55-62
Abstract: 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.
Keywords: Formal Concept Analysis, Conceptual Knowledge Processing, conceptual hierarchies, classical reduction methods