Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of text retrieval models
The Computer Journal - Special issue on information retrieval
An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
Exploration of text collections with hierarchical feature maps
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Self-organizing maps
Readings in information retrieval
Readings in information retrieval
Information forage through adaptive visualization
Proceedings of the third ACM conference on Digital libraries
Self-Organizing Maps
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Modern Information Retrieval
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Pattern Based Browsing in Document Collections
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Mining in the Phrasal Frontier
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Self-Organization of Distributed Document Archives
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A survey on swarm and evolutionary algorithms for web mining applications
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
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Today's information age may be characterized by constant massive production and dissemination of written information. More powerful tools for exploring, searching, and organizing the available mass of information are needed to cope with this situation. This need is our starting point for applying data mining techniques on unstructured information as present in text archives. The users will particularly benefit from cluster techniques that uncover similar documents and bring these similarities to the user's attention. In our approach to text mining we suggest relying on the utilization of self-organizing maps for the analysis of a document archive. The benefit of this approach is the intuitive visualization of document similarities thanks to the spatial ordering of the documents within the self-organizing map. We augment the basic capabilities of the neural network with a data description technique that, based on the features learned by the map, automatically selects the most descriptive features of the input patterns mapped onto a particular unit of the map, thus making the associations between the various clusters within the map explicit. We demonstrate the benefits of this approach by using a real-world document archive comprised of articles from Time magazine.