Scalable Performance of FCbO Algorithm on Museum Data

Formal Concept Analysis – known as a technique for data analysis and visualisation – can also be applied as a means of creating interaction approaches that allow for knowledge discovery within collections of content. These interaction approaches rely on performant algorithms that can generate conceptual neighbourhoods based on a single formal concept, or incrementally compute and update a set of formal concepts given changes to a formal context. Using case studies based on content from museum collections, this paper describes the scalability limitations of existing interaction approaches and presents an implementation and evaluation of the FCbO update algorithm as a means of updating formal concepts from large and dynamically changing museum datasets.