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English Information

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Authors
# Name
1 Régis Sousa(regismaicon@gmail.com)
2 Humberto Razente(humberto.razente@ufu.br)
3 Maria Camila Barioni(camila.barioni@ufu.br)

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Reference
# Reference
1 Ciaccia, P., Patella, M., and Zezula, P. (1997). M-tree: An efficient access method for similarity search in metric spaces. In VLDB, pages 426–435, Atenas, Grécia.
2 Gama, J. (2012). A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence, 1(1):45–55.
3 Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In SIGMOD, pages 47–57, Boston, Massachusetts.
4 Lichman, M. (2013). UCI Machine Learning Repository, University of California, Irvine, http://archive.ics.uci.edu/ml.
5 Navarro, G. and Reyes, N. (2016). New dynamic metric indices for secondary memory. Information Systems, 59:48–78.
6 Oliveira, P., Traina, C., and Kaster, D. (2015). Improving the pruning ability of dynamic metric access methods with local additional pivots and anticipation of information. In ADBIS, LNCS 9282, pages 18–31, Poitiers, França. Springer.
7 Skopal, T. (2006). On fast non-metric similarity search by metric access methods. In EDBT, LNCS 3896, pages 718–736, Munique, Alemanha. Springer.
8 Souza, J., Razente, H., and Barioni, M. C. (2014). Optimizing metric access methods for querying and mining complex data types. J. Braz. Comput. Soc., 20(1):1.
9 Traina, C., Traina, A., Faloutsos, C., and Seeger, B. (2002). Fast indexing and visualization of metric data sets using slim-trees. IEEE Trans Knowl Data Eng, 14(2):244–260.
10 Vieira, M. R., Jr., C. T., Chino, F. J. T., and Traina, A. J. M. (2010). Dbm-tree: A dynamic metric access method sensitive to local density data. JIDM, 1(1):111–128.