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Sparck Jones, \u201cA statistical interpretation of term specificity and its application in retrieval,\u201d Journal of Documentation, vol.28, no.1, pp.11-21, 1972. 10.1108\/eb026526","DOI":"10.1108\/eb026526"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] R. Cummins and C. O'riordan, \u201cEvolving general term-weighting schemes for information retrieval: Tests on larger collections,\u201d Artificial Intelligence Review, vol.24, no.3-4, pp.277-299, 2005. 10.1007\/s10462-005-9001-y","DOI":"10.1007\/s10462-005-9001-y"},{"key":"5","unstructured":"[5] K. Wagstaff, C. Cardie, S. Rogers, S. Schr\u00f6dl, et al., \u201cConstrained k-means clustering with background knowledge,\u201d Proceedings of the 18th International Conference on Machine Learning, pp.577-584, 2001."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] M. Bilenko, S. Basu, and R.J. Mooney, \u201cIntegrating constraints and metric learning in semi-supervised clustering,\u201d Proceedings of the Twenty-first International Conference on Machine Learning, p.11, ACM, 2004. 10.1145\/1015330.1015360","DOI":"10.1145\/1015330.1015360"},{"key":"7","unstructured":"[7] H. Zhang, S. Basu, and I. Davidson, \u201cDeep constrained clustering-algorithms and advances,\u201d arXiv preprint arXiv:1901.10061, 2019."},{"key":"8","unstructured":"[8] S. Basu, A. Banerjee, and R.J. Mooney, \u201cSemi-supervised clustering by seeding,\u201d Proceedings of the 19th International Conference on Machine Learning, ICML '02, pp.27-34, 2002."},{"key":"9","unstructured":"[9] D. Klein, S.D. Kamvar, and C.D. 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Verde, and F.d.A.T. de Carvalho, \u201cFuzzy clustering of distributional data with automatic weighting of variable components,\u201d Information Sciences, vol.406, pp.248-268, 2017. 10.1016\/j.ins.2017.04.040","DOI":"10.1016\/j.ins.2017.04.040"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] T.-P. Lo and S.-J. Guo, \u201cEffective weighting model based on the maximum deviation with uncertain information,\u201d Expert Systems with Applications, vol.37, no.12, pp.8445-8449, 2010. 10.1016\/j.eswa.2010.05.034","DOI":"10.1016\/j.eswa.2010.05.034"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] M.A. Fattah, \u201cNew term weighting schemes with combination of multiple classifiers for sentiment analysis,\u201d Neurocomputing, vol.167, pp.434-442, 2015. 10.1016\/j.neucom.2015.04.051","DOI":"10.1016\/j.neucom.2015.04.051"},{"key":"24","unstructured":"[24] K. Nigam, J. Lafferty, and A. 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