Graded concepts and associations
Trevor Martin, Ben Azvine · 2017
In the current data-rich / knowledge-poor world, humans require machine assistance to summarize, analyze and understand a situation and the trends in events. The idea of collaborative intelligence enables humans to focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. In this paper, we propose graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. Graded concepts allow summarization at multiple levels of discernibility (granularity). We present a novel incremental algorithm to find a graded concept lattice. The lattice can be used to identify associations in data at multiple levels of discernibility.