Abstract
The biases of individual algorithms for non-parametric document clustering can lead to non-optimal solutions. Ensemble clustering methods may overcome this limitation, but have not been applied to document collections. This paper presents a comparison of strategies for non-parametric document ensemble clustering.
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References
Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proc. of CIKM (2002)
Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3) (2004)
Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrica 50 (1985)
Li, T., Ma, S., Ogihara, M.: Document clustering via adaptive subspace iteration. In: Proc. of SIGIR (2004)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society Series B 63(2) (2001)
Fraley, C., Raftery, A.: How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal 41(8) (1998)
Surdeanu, M., Turmo, J., Ageno, A.: A hybrid unsupervised approach for document clustering. In: Proc. of KDD (2005)
Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: Models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12) (2005)
Strehl, A., Ghosh, J.: Cluster ensembles - A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3 (2002)
Siersdorfer, S., Sizov, S.: Restrictive clustering and metaclustering for self-organizing document collections. In: Proc. of SIGIR (2004)
Greene, D., Cunningham, P.: Efficient ensemble methods for document clustering. Technical report, Department of Computer Science, Trinity College Dublin (2006)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. In: Proc. of ICDE (2005)
Fred, A., Jain, A.: Robust data clustering. In: Proc. of CVPR (2003)
Li, T., Ogihara, M., Ma, S.: On combining multiple clusterings. In: Proc. of CIKM (2004)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3) (2000)
Slonim, N.: The Information Bottleneck: Theory and Applications. PhD thesis, The Hebrew University (2003)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Communications in Statistics 3 (1974)
Dhillon, I., Guan, Y.: Information theoretic clustering of sparse co-occurrence data. In: Proc. of ICDM (2003)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006)
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Gonzàlez, E., Turmo, J. (2008). Comparing Non-parametric Ensemble Methods for Document Clustering. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds) Natural Language and Information Systems. NLDB 2008. Lecture Notes in Computer Science, vol 5039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69858-6_25
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DOI: https://doi.org/10.1007/978-3-540-69858-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69857-9
Online ISBN: 978-3-540-69858-6
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