Abstract
In this paper we propose two iterative clustering methods for grouping Wikipedia documents of a given huge collection into clusters. The recursive method clusters iteratively subsets of the complete collection. In each iteration, we select representative items for each group, which are then used for the next stage of clustering.
The presented approaches are scalable algorithms which may be used with huge collections that in other way (for instance, using the classic clustering methods) would be computationally expensive of being clustered. The obtained results outperformed the random baseline presented in the INEX 2010 clustering task of the XML-Mining track.
This work has been partially supported by the CONACYT project #106625, VIEP #PIAD-ING11-I, as well as by the PROMEP/103.5/09/4213 grant.
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© 2011 Springer-Verlag Berlin Heidelberg
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Tovar, M., Cruz, A., Vázquez, B., Pinto, D., Vilariño, D., Montes, A. (2011). An Iterative Clustering Method for the XML-Mining Task of the INEX 2010. In: Geva, S., Kamps, J., Schenkel, R., Trotman, A. (eds) Comparative Evaluation of Focused Retrieval. INEX 2010. Lecture Notes in Computer Science, vol 6932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23577-1_36
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DOI: https://doi.org/10.1007/978-3-642-23577-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23576-4
Online ISBN: 978-3-642-23577-1
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