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Mining frequent subgraphs is an area of research where we have a given set ofgraphs (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 discusstechniques used in our framework Lattice2SAR for mining and analysing frequentsubgraph data and their corresponding lattice information. Lattice informationis provided by the graph mining algorithm gSpan; it contains allsupergraph-subgraph relations of the frequent subgraph patterns -- and theirsupports.  Lattice2SAR is in particular used in the analysis of frequent graph patternswhere the graphs are molecules and the frequent subgraphs are fragments. In theanalysis 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 ofmaking it better available by using the lattice information in our clustering.Now we can reduce the number of times the highly compressed occurrence dataneeds to be accessed by the user. The user does not have to browse all theoccurrence data in search of patterns occurring in the same molecules. Insteadone can directly see which frequent subgraphs are of interest.

Clustering with Lattices in the Analysis of Graph Patterns