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		<property:References rdf:datatype="http://www.w3.org/2001/XMLSchema#string">- Some slides: https://www.siam.org/meetings/sdm11/clustering.pdf

- Muller, E., Gunnemann, S., Farber, I., &amp; Seidl, T. (2012, April). Discovering multiple clustering solutions: Grouping objects in different views of the data. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on (pp. 1207-1210). IEEE.

- Hu, J., &amp; Pei, J. (2017). Subspace multi-clustering: a review. Knowledge and Information Systems, 1-28.

- Yang, S., &amp; Zhang, L. (2017). Non-redundant multiple clustering by nonnegative matrix factorization. Machine Learning, 106(5), 695-712.

- Dang, X. H., &amp; Bailey, J. (2015). A framework to uncover multiple alternative clusterings. Machine Learning, 98(1-2), 7-30.

- Gionis, A., Mannila, H., &amp; Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.

- Qi, Z., &amp; Davidson, I. (2009, June). A principled and flexible framework for finding alternative clusterings. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 717-726). ACM.

- Muller, E., Gunnemann, S., Farber, I., &amp; Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210).

- Cui, Y., Fern, X. Z., &amp; Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on  Data Mining (ICDM), (pp. 133-142).

- Strehl, A., &amp; Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.</property:References>
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