Visualization in stylometry: Cluster analysis using networks. (2nd December 2015)

• Record Type: Journal Article
• Authors: Eder, Maciej
• Is Part Of: Digital scholarship in the humanties. Volume 32:Number 1(2017)
• Page Start: 50
• Page End: 64
• Publication Date: 2015-12-02
• Subjects: Philology -- Data processing -- Periodicals; Computational linguistics -- Periodicals; 410.285
• DOI: 10.1093/llc/fqv061
• Languages: English
• ISSNs: 2055-768X

Abstract

Abstract: 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.