Abstract
The primary aim of most data mining algorithms is to facilitate the
discovery of concise and interpretable information from large amounts
of data. However, many of the current formalizations of data mining
algorithms have not quite reached this goal. One of the reasons
for this is that the focus on using purely automated techniques
has imposed several constraints on data mining algorithms. For example,
any data mining problem such as clustering or association rules
requires the specification of particular problem formulations, objective
functions, and parameters. Such systems fail to take the user's
needs into account very effectively. This makes it necessary to
keep the user in the loop in a way which is both efficient and interpretable.
One unique way of achieving this is by leveraging human visual perceptions
on intermediate data mining results. Such a system combines the
computational power of a computer and the intuitive abilities of
a human to provide solutions which cannot be achieved by either.
This paper will discuss a number of recent approaches to several
data mining algorithms along these lines.
Users
Please
log in to take part in the discussion (add own reviews or comments).