From Crisp to Soft Possibilistic and Rough Meta-clustering of Retail Datasets
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-76348-4_47
Reference13 articles.
1. Ammar, A., Elouedi, Z.: A new possibilistic clustering method: the possibilistic k-modes. In: Proceedings of the 12th International Conference of the Italian Association for Artificial Intelligence. LNAI, vol. 6934, pp. 413–419 (2011)
2. Ammar, A., Elouedi, Z., Lingras, P.: The k-modes method using possibility and rough set theories. In: Proceedings of the IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS, pp. 1297–1302. IEEE (2013)
3. Ammar, A., Elouedi, Z., Lingras, P.: Rough possibilistic meta-clustering of retail datasets. In: Proceedings of the 2014 International Conference on Data Science and Advanced Analytics, DSAA 2014, pp. 177–183. IEEE (2014)
4. Ammar, A., Elouedi, Z., Lingras, P.: Meta-clustering of possibilistically segmented retail datasets. Fuzzy Sets Syst. 286, 173–196 (2016)
5. Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenium Press, New York (1988)
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