Abstract
Clustering text documents is a basic enabling technique in a wide variety of Information and Knowledge Management applications. This paper presents an incremental clustering system to organize and manage Newsgroup articles. It serves administrators and readers of a Newsgroup to archive important postings and to get a structured over-view on current developments and topics. To be practically applicable, such a system must fulfill two conditions. First, it must be able to process rapidly changing text streams, modifying the cluster structure dynamically by adding, deleting and restructuring clusters. Second, it must consider the user in the incremental process. Severe changes in the organization structure are unacceptable for most users, even if they are optimal from the point of view of an abstract clustering criterion. We propose an approach to model the cost to accommodate to changes in the cluster structure explicitly. Users then may constraint, which changes are acceptable to them.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hennig, S., Wurst, M. (2006). Incremental Clustering of Newsgroup Articles. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_37
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DOI: https://doi.org/10.1007/11779568_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
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