[1] Bezdek, J.C., Ehrlich, R, Full, W. (1984) FCM: the fuzzy c-means clustering algorithm, Computers and Geo-sciences , 10 , 191-203.
[2] Berrya, M.W, Browne, M, Langville, A.N, Pauca, V.P and R.J. Plemmons. (2007) Algorithms and applications for approximate nonnegative matrix factorization, Computational Statistics and Data Analysis, 52, 155–173.
[3] Castro, O., Gentile, C., Spagnolo-Arrizabalaga, E. (2022) An algorithm for the microaggregation problem using column generation, Computers and Operations Research,68, 105817.
[4] César Fadel, A., Satoru Ochi, L., André de Moura Brito, J., Silva Semaan, G. (2021)Microaggregation heuristic applied to statistical disclosure control, Information Sciences, 548, 37-55.
[5] Domingo-Ferrer, J., and Mateo-Sanz J.M. (2002) Practical data-oriented microaggregation for statistical disclosure control, IEEE Transactions on Knowledge and Data Engineering, 14, 189-201.
[6] Edgar, B. Antoni,M. Agusti, A. (2022) Privacy-preserving process mining: A microaggregation-based approach, Journal of Information Security and Applications, 68, 103235.
[7] Elden, L. (2007) Matrix Methods in Data Mining and Pattern Recognition, Society for Industrial and Applied Mathematics 106-110.
[8] ]Hansen, S.L, Mukherjee, S. (2003) A polynomial algorithm for optimal univariate microaggregation, in IEEE Transactions on Knowledge and Data Engineering, 4, 1043-1044.
[9] Kiran, A., and Shirisha, N. (2022) K-Anonymization approach for privacy preservation using data perturbation techniques in data mining. Materials Today: Proceedings.
[10] Oganian, A, Domingo-Ferrer, J. (2000) On the Complexity of Optimal Microaggregation for Statistical Disclosure Control, Statistical Journal of the United Nations Economic Commission for Europe, 4, 345–354.
[11] Rodriguez-Garcia, M., Batet, M., Sánchez, D. (2019) Utility-preserving privacy protection of nominal data sets via semantic rank swapping, Information Fusion, 45, 282-295.
[12] Torra, V. (2017) Masking methods. In: Torra, V. (ed.) Data Privacy: Foundations, New Developments and the Big Data Challenge. Studies in Big Data, 28, 191–238.
[13] Torra, V. (2008) Constrained microaggregation: adding constraints for data editing, Transactions on data privacy, 1, 86–104.
[14] Torra, V. (2020) Fuzzy Clustering-based Microaggregation to Achieve Probabilistic K-anonymity for Data with Constraints, Journal of Intelligent and Fuzzy Systems, 39, 5999–6008.
[15] Vaidya, J, Zhu, Y. and C. Clifton, Privacy Preserving Data Mining, in Advances in Information Security, Springer, 19 2006, 1-121.
[16] Yao, A.C. (1982) Protocols for secure computations, 23rd Annual Symposium on Foundations of Computer Science, 160-164.
[17] Wang, Y. X, and Zhang, Y. J. (2013) Nonnegative Matrix Factorization: A Comprehensive Review, in IEEE Transactions on Knowledge and Data Engineering, 6, 1336-1353.
[18]https://archive.ics.uci.edu/ml/datasets/wine.
[19]https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original).
[20]https://archive.ics.uci.edu/ml/datasets/iris.
[21]https://archive.ics.uci.edu/ml/datasets/haberman’s+survival.
[22]https://archive.ics.uci.edu/ml/datasets/glass+identification.