The aim of this article is to discuss reliability issues of a few visual techniques used in stylometry, and to introduce a new method that enhances the explanatory power of visualization with a procedure of validation inspired by advanced statistical methods. A promising way of extending cluster analysis dendrograms with a self-validating procedure involves producing numerous particular ‘snapshots’, or dendrograms produced using different input parameters, and combining them all into the form of a consensus tree. Significantly better results, however, can be obtained using a new visualization technique, which combines the idea of nearest neighborhood derived from cluster analysis, the idea of hammering out a clustering consensus from bootstrap consensus trees, with the idea of mapping textual similarities onto a form of a network. Additionally, network analysis seems to be a good solution for large data sets.
['Maciej Eder']
Visualization in Stylometry: Cluster Analysis Using Networks