Analysis of large data sets using formal concept lattices
Simon Andrews, Constantinos Orphanides · SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) · 2010
Abstract. Formal Concept Analysis (FCA) is an emerging data technology that has applications in the visual analysis of large-scale data. However, data sets are often too large (or contain too many formal concepts) for the resulting concept lattice to be readable. This paper complements existing work in this area by describing two methods by which useful and manageable lattices can be derived from large data sets. This is achieved though the use of a set of freely available FCA tools: the context creator FcaBedrock and the concept miner In-Close, that were developed by the authors, and the lattice builder ConExp. In the first method, a sub-context is produced from a data set, giving rise to a readable lattice that focuses on attributes of interest. In the second method, a context is mined for ‘large ’ concepts which are then used to re-write the original context, thus reducing ‘noise ’ in the context and giving rise to a readable lattice that lucidly portrays a conceptual overview of the large set of data it is derived from. 1