  Association Rule Mining is a machine learning method for discovering the
interesting relations between the attributes in a huge transaction database.
Typically, algorithms for Association Rule Mining generate a huge number of
association rules, from which it is hard to extract structured knowledge and
present this automatically in a form that would be suitable for the user.
Recently, an information cartography has been proposed for creating structured
summaries of information and visualizing with methodology called "metro maps".
This was applied to several problem domains, where pattern mining was
necessary. The aim of this study is to develop a method for automatic creation
of metro maps of information obtained by Association Rule Mining and, thus,
spread its applicability to the other machine learning methods. Although the
proposed method consists of multiple steps, its core presents metro map
construction that is defined in the study as an optimization problem, which is
solved using an evolutionary algorithm. Finally, this was applied to four
well-known UCI Machine Learning datasets and one sport dataset. Visualizing the
resulted metro maps not only justifies that this is a suitable tool for
presenting structured knowledge hidden in data, but also that they can tell
stories to users.
