Abstract
Deriving a thematically meaningful partition of an unlabeled text corpus is a challenging task. In comparison to classic term-based document indexing, the use of document representations based on latent thematic generative models can lead to improved clustering. However, determining a priori the optimal indexing technique is not straightforward, as it depends on the clustering problem faced and the partitioning strategy adopted. So as to overcome this indeterminacy, we propose deriving a consensus labeling upon the results of clustering processes executed on several document representations. Experiments conducted on subsets of two standard text corpora evaluate distinct clustering strategies based on latent thematic spaces and highlight the usefulness of consensus clustering to overcome the optimal document indexing indeterminacy.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. J. American Society Information Science 6(41), 391–407 (1990)
Fred, A., Jain, A.K.: Combining Multiple Clusterings Using Evidence Accumulation. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(6), 835–850 (2005)
Hersh, W., Buckley, C., Leone, T., Hichman, D.: OHSUMED: an interactive retrieval evaluation and new large text collection for research. In: Proc. of the 17th ACM SIGIR Conference, pp. 192–201 (1994)
Hettich, S., Bay, S.D.: The UCI KDD Archive. University of California at Irvine, Dept. of Information and Computer Science (1999), http://kdd.ics.uci.edu
Hoyer, P.O.: Non-Negative Matrix Factorization with Sparseness Constraints. J. Machine Learning Research 5, 1457–1469 (2004)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: a Survey. ACM Computing Surveys 31(3), 264–323 (1999)
Kabán, A., Girolami, M.: Unsupervised Topic Separation and Keyword Identification in Document Collections: a Projection Approach. Dept. of Computing and Information Systems, University of Paisley. Technical Report Nr. 10 (2000)
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)
Kolenda, T.: Clustering text using Independent Component Analysis. Inst. of Informatics and Mathematical Modelling, Tech. University of Denmark. T.R (2002)
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 Categorisation. ACM Computing Surveys 34(1), 1–47 (2002)
Sevillano, X., Alías, F., Socoró, J.C.: Reliability in ICA-Based Text Classification. In: Proc. of the 5th Intl. Conference on Independent Component Analysis and Blind Signal Separation, pp. 1210–1217 (2004)
Sevillano, X., Cobo, G., Alías, F., Socoró, J.C.: A Hierarchical Consensus Architecture for Robust Document Clustering. In: Proc. of the 29th ECIR Conference, pp. 741–744 (2007)
Shafiei, M., Wang, S., Zhang, R., Milios, E., Tang, B., Tougas, J., Spiteri, R.: A Systematic Study of Document Representation and Dimension Reduction for Text Clustering. Technical Report CS-2006-05. Dalhousie University (2006)
Siersdorfer, S., Sizov, S.: Restrictive Clustering and Metaclustering for Self-Organizing Document Collections. In: Proc. of the 27th ACM SIGIR Conference, pp. 226–233 (2004)
Strehl, A., Ghosh, J.: Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions. J. Machine Learning Research 3, 583–617 (2002)
Tang, B., Shepherd, M., Milios, E., Heywood, M.I.: Comparing and Combining Dimension Reduction Techniques for Efficient Text Clustering. In: Proc. of the Intl. Workshop on Feature Selection for Data Mining, pp. 17–26 (2005)
Xu, W., Liu, X., Gong, Y.: Document Clustering Based on Non-Negative Matrix Factorization. In: Proc. of the 26th ACM SIGIR Conference, vol. 2, pp. 267–273 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sevillano, X., Cobo, G., Alías, F., Socoró, J.C. (2007). Text Clustering on Latent Thematic Spaces: Variants, Strengths and Weaknesses. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_99
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)