
Knowledge discovery is important for systems\ud that have computational intelligence in helping them learn\ud and adapt to changing environments. By representing, in\ud a formal way, the context in which an intelligent system\ud operates, it is possible to discover knowledge through an\ud emerging data technology called Formal Concept Analysis\ud (FCA). This paper describes a tool called FcaBedrock that\ud converts data into Formal Contexts for FCA. The paper\ud describes how, through a process of guided automation,\ud data preparation techniques such as attribute exclusion and\ud value restriction allow data to be interpreted to meet the requirements\ud of the analysis. Creating Formal Contexts using\ud FcaBedrock is shown to be straightforward and versatile.\ud Large data sets are easily converted into a standard FCA\ud format.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
