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Alejo, L., Atkinson, J., & Lackner, S. (2020). Looking deeper – exploring hidden patterns in reactor data of N-removal systems through clustering analysis. Water Sci. Technol., 81(8), 1569–1577.
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Allende, C., Sohn, E., & Little, C. (2015). Treelink: data integration, clustering and visualization of phylogenetic trees. BMC Bioinformatics, 16, 6 pp.
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Araya-Diaz, P., Ruz, G. A., & Palomino, H. M. (2013). Discovering Craniofacial Patterns Using Multivariate Cephalometric Data for Treatment Decision Making in Orthodontics. Int. J. Morphol., 31(3), 1109–1115.
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Baler, R. V., Wijnhoven, I. B., del Valle, V. I., Giovanetti, C. M., & Vivanco, J. F. (2019). Microporosity Clustering Assessment in Calcium Phosphate Bioceramic Particles. Front. Bioeng. Biotechnol., 7(281), 7 pp.
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Canessa, E., Chaigneau, S. E., Moreno, S., & Lagos, R. (2020). Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data. Cogn. Process., 21, 601–614.
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Fierro, R., Leiva, V., & Moller, J. (2015). The Hawkes Process With Different Exciting Functions And Its Asymptotic Behavior. J. Appl. Probab., 52(1), 37–54.
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Garreton, M., & Sanchez, R. (2016). Identifying an optimal analysis level in multiscalar regionalization: A study case of social distress in Greater Santiago. Comput. Environ. Urban Syst., 56, 14–24.
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Heredia, C., Moreno, S., & Yushimito, W. F. (2021). Characterization of Mobility Patterns with a Hierarchical Clustering of Origin-Destination GPS Taxi Data. IEEE Trans. Intell. Transp. Syst., Early Access.
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Moreno, S., Pereira, J., & Yushimito, W. (2020). A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution. Ann. Oper. Res., 286(1-2), 87–117.
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Pham, D. T., & Ruz, G. A. (2009). Unsupervised training of Bayesian networks for data clustering. Proc. R. Soc. A-Math. Phys. Eng. Sci., 465(2109), 2927–2948.
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Ruz, G. A. (2016). Improving the performance of inductive learning classifiers through the presentation order of the training patterns. Expert Syst. Appl., 58, 1–9.
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Ruz, G. A., & Pham, D. T. (2012). NBSOM: The naive Bayes self-organizing map. Neural Comput. Appl., 21(6), 1319–1330.
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Ruz, G. A., Varas, S., & Villena, M. (2013). Policy making for broadband adoption and usage in Chile through machine learning. Expert Syst. Appl., 40(17), 6728–6734.
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Valle, M. A., & Ruz, G. A. (2021). Finding Hierarchical Structures of Disordered Systems: An Application for Market Basket Analysis. IEEE Access, 9, 1626–1641.
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