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English Information

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Authors
# Name
1 Mateus de Lima(mateuscurcino@mestrado.ufu.br)
2 Maria Camila Barioni(camila.barioni@ufu.br)
3 Humberto Razente(humberto.razente@ufu.br)

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Reference
# Reference
1 Basu, S., Davidson, I., and Wagstaff, K. (2008). Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC, 1 edition.
2 Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 7:1–30.
3 Dhillon, I. S., Guan, Y., and Kulis, B. (2004). Kernel k-means: Spectral clustering and normalized cuts. KDD ’04, pages 551–556. ACM.
4 Faceli, K., Lorena, A. C., Gama, J. a., and Carvalho, A. (2011). Inteligência Artificial: Uma Abordagem de Aprendizado de Máquina. LTC, 1 edition.
5 Samet, H. (2005). Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann.
6 Sander, J., Ester, M., Kriegel, H.-P., and Xu, X. (1998). Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Min. Knowl. Discov., 2(2):169–194.
7 Silvestre, A. L. (2007). Análise de Dados e Estatística Descritiva. Escolar Editora.
8 Tomasev, N. and Mladenic, D. (2013). Hub co-occurrence modeling for robust high-dimensional knn classification. In ECML PKDD, pages 643–659. Springer.
9 Tomasev, N., Radovanovic, M., Mladenic, D., and Ivanovic, M. (2011). The role of hubness in clustering high-dimensional data. PAKDD, pages 183–195. Springer.
10 Tomasev, N., Radovanovic, M., Mladenic, D., and Ivanovic, M. (2014). The role of hubness in clustering high-dimensional data. IEEE TKDE, 26(3):739–751.
11 Zar, J. H. (2007). Biostatistical Analysis. Prentice-Hall, Inc., 5 edition.