Abstract
Constrained clustering is a recently presented family of semi-supervised learning algorithms. These methods use domain information to impose constraints over the clustering output. The way in which those constraints (typically pair-wise constraints between documents) are introduced is by designing new clustering algorithms that enforce the accomplishment of the constraints. In this paper we present an alternative approach for constrained clustering where, instead of defining new algorithms or objective functions, the constraints are introduced modifying the document representation by means of their language modelling. More precisely the constraints are modelled using the well-known Relevance Models successfully used in other retrieval tasks such as pseudo-relevance feedback. To the best of our knowledge this is the first attempt to try such approach. The results show that the presented approach is an effective method for constrained clustering even improving the results of existing constrained clustering algorithms.
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References
Abdul-jaleel, N., Allan, J., Croft, W.B., Diaz, O., Larkey, L., Li, X., Smucker, M.D., Wade, C.: UMass at trec 2004: Novelty and hard. In: Proceedings of TREC-13 (2004)
Ares, M.E., Parapar, J., Barreiro, Á.: Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 322–329. Springer, Heidelberg (2009)
Bae, E., Bailey, J.: Coala: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In: ICDM 2006, pp. 53–62 (2006)
Balasubramanian, N., Allan, J., Croft, W.B.: A comparison of sentence retrieval techniques. In: ACM SIGIR 2007, pp. 813–814 (2007)
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with bregman divergences. J. Mach. Learn. Res. 6, 1705–1749 (2005)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: ACM KDD 2004, pp. 59–68 (2004)
Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC (2008)
Conover, W.J.: Practical nonparametric statistics, 3rd edn. John Wiley & Sons, New York (1971)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985)
Ji, X., Xu, W.: Document clustering with prior knowledge. In: ACM SIGIR 2006, pp. 405–412 (2006)
Jin, R., Ding, C., Kang, F.: A probabilistic approach for optimizing spectral clustering. In: Advances in Neural Information Processing Systems, vol. 18 (2005)
Klein, D., Kamvar, S., Manning, C.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: ICML, pp. 307–314 (2002)
Lavrenko, V., Croft, W.B.: Relevance based language models. In: ACM SIGIR, pp. 120–127 (2001)
Lee, K.S., Croft, W.B., Allan, J.: A cluster-based resampling method for pseudo-relevance feedback. In: ACM SIGIR 2008, pp. 235–242 (2008)
Li, X., Zhu, Z.: Enhancing Relevance Models with Adaptive Passage Retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 463–471. Springer, Heidelberg (2008)
Lv, Y., Zhai, C.: A comparative study of methods for estimating query language models with pseudo feedback. In: ACM CIKM 2009, pp. 1895–1898 (2009)
MacQueen, J.B.: 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)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: ICML 2000, pp. 1103–1110 (2000)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: ICML 2001, pp. 577–584 (2001)
Wang, F., Li, T., Zhang, C.: Semi-supervised clustering via matrix factorization. In: SDM 2008, pp. 1–12 (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 (2002)
Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., Ma, J.: Learning to cluster web search results. In: ACM SIGIR 2004, pp. 210–217 (2004)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004)
Zhai, Z., Liu, B., Xu, H., Jia, P.: Constrained LDA for Grouping Product Features in Opinion Mining. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS, vol. 6634, pp. 448–459. Springer, Heidelberg (2011)
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Parapar, J., Barreiro, Á. (2012). Language Modelling of Constraints for Text Clustering. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_30
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DOI: https://doi.org/10.1007/978-3-642-28997-2_30
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