Abstract
A major problem encountered by text clustering practitioners is the difficulty of determining a priori which is the optimal text representation and clustering technique for a given clustering problem. As a step towards building robust document partitioning systems, we present a strategy based on a hierarchical consensus clustering architecture that operates on a wide diversity of document representations and partitions. The conducted experiments show that the proposed method is capable of yielding a consensus clustering that is comparable to the best individual clustering available even in the presence of a large number of poor individual labelings, outperforming classic non-hierarchical consensus approaches in terms of performance and computational cost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deerwester, S., et al.: Indexing by Latent Semantic Analysis. Journal American Society Information Science 6(41), 391–407 (1990)
Kolenda, T., Hansen, L.K., Sigurdsson, S.: Independent Components in Text. In: Girolami, M. (ed.) Advances in Independent Component Analysis, pp. 241–262. Springer, Heidelberg (2000)
Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 401, 788–791 (1999)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Shafiei, M., et al.: A Systematic Study of Document Representation and Dimension Reduction for Text Clustering. Technical Report CS-2006-05. Dalhousie University (2006)
Strehl, A., Ghosh, J.: Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions. JMLR 3, 583–617 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Sevillano, X., Cobo, G., Alías, F., Socoró, J.C. (2007). A Hierarchical Consensus Architecture for Robust Document Clustering. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_82
Download citation
DOI: https://doi.org/10.1007/978-3-540-71496-5_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
eBook Packages: Computer ScienceComputer Science (R0)