Abstract
In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effectiveness against previous constrained and non-constrained algorithms for the given task.
This is a preview of subscription content, log in via an institution to check access.
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
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC, Boca Raton (2008)
Yang, H., Callan, J.: Near-duplicate detection by instance-level constrained clustering. In: Proc. of SIGIR 2006, pp. 421–428 (2006)
Ji, X., Xu, W.: Document clustering with prior knowledge. In: Proc. of SIGIR 2006, pp. 405–412 (2006)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proc. of ICML 2000, pp. 1103–1110 (2000)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proc. of ICML 2001, pp. 577–584 (2001)
Klein, D., Kamvar, S.D., Manning, C.D.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proc. of ICML 2002, pp. 307–314 (2002)
Gondek, D., Hofmann, T.: Non-redundant data clustering. In: Proc. of ICDM 2004, pp. 75–82 (2004)
Bae, E., Bailey, J.: COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In: Proc. of ICDM 2006, pp. 53–62 (2006)
Davidson, I., Qi, Z.: Finding alternative clustering using constraints. In: Proc. of ICDM 2008, pp. 773–778 (2008)
Cui, Y., Fern, X.Z., Dy, J.G.: Non-redundant multi-view clustering via orthogonalization. In: Proc. of ICDM 2007, pp. 133–142 (2007)
McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Pantel, P., Lin, D.: Document clustering with committees. In: Proc. of SIGIR 2002, pp. 199–206 (2002)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems, vol. 15, pp. 505–512. MIT Press, Cambridge (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ares, M.E., Parapar, J., Barreiro, Á. (2009). Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04417-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-04417-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04416-8
Online ISBN: 978-3-642-04417-5
eBook Packages: Computer ScienceComputer Science (R0)