Abstract
Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as consistent when contradiction associated with the data is not visualised. This work identifies incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis (FCA) tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data (IID) when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose objects are associated with mutually exclusive many-valued attributes, to a formal context.
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