Abstract:
Visual data mining is a promising way of dealing with the complexity of integrated data sets of various granularity
and sizes. It looks at having an access to the entire data set in its most granular level. This paper presents a view of
the visual data mining as a "reflection-in-action" technique. We have illustrated two different types of this
technique, namely "guided cognition" and "validated cognition". This work is motivated by the fact that visual,
though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an
understandable alternative to the highly complex statistical approaches) without the ability to understand their
applicability and limitations.
