Bibliografia

Nwanganga, F., M. Chapple (2020), Practical Machine Learning in R, 1st Edition, Wiley. Bouveyron, C., G. Celeux, T. B. Murphy, A. E. Raftery (2019), Model-Based Clustering and Classification for Data Science: With Applications in R, 1st Edition, Cambridge University Press. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning: with applications in R, New York: Springer. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2014), Multivariate Data Analysis, 7th Edition, Essex, UK: Pearson Education.:

Wedel, M., Kamakura, W. A. (2000), Market Segmentation. Conceptual and Methodological Foundations (2nd edition), International Series in Quantitative Marketing. Boston: Kluwer Academic Publishers. Lattin, J., D. Carroll e P. Green (2003), Analyzing Multivariate Data, Pacific Grove, CA: Thomson Learning. Kohonen, T. (2001). Self-Organizing Maps. Third edition, Springer. Hennig, C., Meila, M., Murtagh, F., Rocci, R. (eds.) (2016), Handbook of Cluster Analysis, Handbooks of Modern Statistical Methods. Boca Raton: Chapman & Hall/CRC. Aggarwal, C. C., Reddy, C. K. (eds.) (2014), Data Clustering: Algorithms and Applications. Boca Raton: CRC Press.: