Abstract
In this paper, we introduce a new clustering algorithm for obtaining labeled document clusters that accurately identify the topics of a text collection. In order to determine the topics, our approach relies on both probable term pairs generated from the collection and the estimation of the topic homogeneity associated to term pair clusters. Experimental results obtained over two benchmark text collections demonstrate the utility of this new approach.
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Anaya-Sánchez, H., Pons-Porrata, A., Berlanga-Llavori, R. (2008). A New Document Clustering Algorithm for Topic Discovering and Labeling. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_20
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DOI: https://doi.org/10.1007/978-3-540-85920-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85919-2
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