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Automated unsupervised authorship analysis using evidence accumulation clustering

Published online by Cambridge University Press:  21 November 2011

ROBERT LAYTON ,
PAUL WATTERS  and
RICHARD DAZELEY
ROBERT LAYTON
Affiliation:
Internet Commerce Security Laboratory, University of Ballarat, Australia e-mails: r.layton@icsl.com.au, p.watters@ballarat.edu.au
PAUL WATTERS
Affiliation:
Internet Commerce Security Laboratory, University of Ballarat, Australia e-mails: r.layton@icsl.com.au, p.watters@ballarat.edu.au
RICHARD DAZELEY
Affiliation:
Data Mining and Informatics Research Group, University of Ballarat, Australia e-mail: r.dazeley@ballarat.edu.au
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Abstract

Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents.

Type
Articles
Information
Natural Language Engineering , Volume 19 , Issue 1 , January 2013 , pp. 95 - 120
Copyright
Copyright © Cambridge University Press 2011

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