  Mining frequent subgraphs is an area of research where we have a given set of
graphs (each graph can be seen as a transaction), and we search for (connected)
subgraphs contained in many of these graphs. In this work we will discuss
techniques used in our framework Lattice2SAR for mining and analysing frequent
subgraph data and their corresponding lattice information. Lattice information
is provided by the graph mining algorithm gSpan; it contains all
supergraph-subgraph relations of the frequent subgraph patterns -- and their
supports.
  Lattice2SAR is in particular used in the analysis of frequent graph patterns
where the graphs are molecules and the frequent subgraphs are fragments. In the
analysis of fragments one is interested in the molecules where patterns occur.
This data can be very extensive and in this paper we focus on a technique of
making it better available by using the lattice information in our clustering.
Now we can reduce the number of times the highly compressed occurrence data
needs to be accessed by the user. The user does not have to browse all the
occurrence data in search of patterns occurring in the same molecules. Instead
one can directly see which frequent subgraphs are of interest.
