Atelier Visualisation et extraction de connaissances - Irisa

4 Open Issues
FIG. 3. A “hot box” is popped out when a concept is clicked.
C.Melo et M-A.Aufaure
A couple of questions and remarks appeared during the research presented in this work and it
may be fruitful to discuss them.
• We have been discussing whether trees were good candidates for the lattice structure.
They are “fully” hierarchical in the sense that a child belongs to only one parent and
hence a parent selection criteria would be necessary to transform lattices into trees. We
will discuss this issue in another upcoming publication;
• Although it is not the focus of this paper, we have concerns about the performance of
drawing algorithms for large lattice exploration. As the visualization is supposed to provide
real-time interaction, updates on the lattice structure should be done in a very short
time. One solution will probably lead to progressively loading the structure as the user
navigates (similarly to GoogleMaps), instead of loading it all at once;
• A closer integration with context reduction techniques will be necessary, including techniques
such as sub-context mining, noise reduction and clustering;
• Selection and manipulation techniques must also be improved e.g., to allow multiple
concepts selection, criteria filter, concept merging and dynamic querying from the lattice.
5 Conclusion
In this work we presented a FCA-based visual analytics tool called CUBIST analytics. We
believe our approach is a first step to address large lattices visualization. We have implemented
a couple of alternatives for lattice exploration; a navigational Fish Eye using combination
of a key and the mouse; and an insitu “hot box” for concept interaction. Traditional
methods for visualization of large lattices are quite restricted to Hasse diagrams. New chal-
F.Poulet, B.Le Grand : 9e Atelier Visualisation et Extraction de Connaissances 53