Abstract
The aim of this paper is to use unsupervised classification techniques in order to group the documents of a given huge collection into clusters. We approached this challenge by using a simple clustering algorithm (K-Star) in a recursive clustering process over subsets of the complete collection.
The presented approach is a scalable algorithm which may automatically discover the number of clusters. The obtained results outperformed different baselines presented in the INEX 2009 clustering task.
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Pinto, D., Tovar, M., Vilariño, D., Beltrán, B., Jiménez-Salazar, H., Campos, B. (2010). BUAP: Performance of K-Star at the INEX’09 Clustering Task. In: Geva, S., Kamps, J., Trotman, A. (eds) Focused Retrieval and Evaluation. INEX 2009. Lecture Notes in Computer Science, vol 6203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14556-8_43
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DOI: https://doi.org/10.1007/978-3-642-14556-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14555-1
Online ISBN: 978-3-642-14556-8
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