Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Visualising class distribution on self-organising maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
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The Self-Organising Map has been frequently employed to organise collections of digital documents, especially textual documents. SOMs can be employed to analyse the content and relations between the documents in a collection, providing an intuitive access to large collections. In this paper, we apply this approach to analysing documents from the Internet platform WikiLeaks. This document collection is interesting for such an analysis for several aspects. For one, the documents contained cover a rather large time-span, thus there should also be an quite divergence in the topics discussed. Further, the documents stem from a magnitude of different sources, thus different styles should be expected. Moreover, the documents have very interesting, previously unpublished content. Finally, while the WikiLeaks website provides a way to browse all documents published by certain meta-data categories such as creation year and origin of the cable, there is no way to access the documents by their content. Thus, the SOM offers a valuable alternative mean to provide access to the content of the collection by their content. For analysing the document collection, we employ the Java SOMToolbox framework, which provides the user with a wealth of analysis and interaction methods, such as different visualisations, zooming and panning, and automatic labelling on different levels of granularity, to help the user in quickly getting an overview of and navigating in the collection.