Visual and analytical extensions for the table lens

Many visualization approaches teach us that ease of use is the key to effective visual data analysis. The Table Lens is an excellent example of a simple, yet expressive visual method that can help in analyzing even larger volumes of data. In this work, we present two extensions of the original Table Lens approach. In particular, we extend the Table Lens by Two-Tone Pseudo Coloring (TTPC) and a hybrid clustering. By integrating TTPC into the Table Lens, we obtain visual representations that can communicate larger volumes of data while still maintaining precision. Secondly, we propose to integrate a data analysis step that implements a hybrid clustering based on self-organizing maps and hierarchical clustering. The analysis step helps to extract and communicate complementary structural information about the data and also serves to drive interactive information drill-down.