One of the most important problems with rule inductionmethods is
that it is very difficult for domain experts to check millions of rules
generated from large datasets, although the discovery from these rules requires
deep interpretation from domain knowledge. Although several solutions have been
proposed in the studies on data mining and knowledge discovery, these studies
are not focused on similarities between rules obtained. When one rule
r_1
has
reasonable features and the other rule
r_2
with high similarity to
r_1
includes
unexpected factors, the relations between these rules will become a trigger to
the discovery of knowledge. In this paper, we propose a visualization approach
to show the similarity relations between rules based on multidimensional
scaling, which assign a two-dimensional cartesian coordinate to each data point
from the information about similarities between this data and others data. We
evaluated this method on two medical data sets, whose experimental results show
that knowledge useful for domain experts can be found.
