Triadic concept analysis (TCA) is an extension of formal concept analysis (FCA), which can be applied to machine learning, data mining, information retrieval, and so on. This paper summarizes the current situation and the development tendency of TCA. We introduce TCA in this paper from four aspects: basic notions of triadic concept analysis, triadic implications and triadic association rules, triadic factor analysis and triadic fuzzy concept analysis.
Formal concept analysis (FCA) is an efficient tool for knowledge discovery and decision making from formal contexts. However, in the era of big data, FCA may face some challenges, one of which is that discovering knowledge from a big formal context may be hard. To make knowledge discovery from formal contexts easier and simpler, this study presents concept analysis based on granular formal contexts. First, granular formal context is proposed by combining FCA with the hierarchical idea of granular computing (GrC). Then, based on which, the corresponding notions such as granular derivation operators, granular formal concept, and granular concept lattice are defined. Finally, the connections between classical and granular derivation operators/formal concepts/concept lattices are presented.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.