MiDaS: Extract Golden Results from Knowledge Discovery Even over Incomplete Databases

Author:

Rodrigues Lucas S.,Vespa Thiago G.,Eleutério Igor A. R.,Oliveira Willian D.,Traina Agma J. M.,Traina Caetano

Publisher

Springer International Publishing

Reference19 articles.

1. Al Marri, W.J., et al.: The similarity-aware relational database set operators. Inf. Syst. 59, 79–93 (2016). https://doi.org/10.1016/j.is.2015.10.008

2. Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

3. Barioni, M.C.N., et al.: SIREN: a similarity retrieval engine for complex data. In: The 32nd International Conference on VLDB, pp. 1155–1158. ACM (2006)

4. Batista, G., et al.: A study of k-nearest neighbour as an imputation method. In: Soft Computing Systems - Design, Management and Applications. Frontiers in Artificial Intelligence and Applications, vol. 87, pp. 251–260. IOS Press (2002)

5. Bedo, M.V.N., Traina, A.J.M., Traina-Jr., C.: Seamless integration of distance functions and feature vectors for similarity-queries processing. JIDM 5(3), 308–320 (2014). https://periodicos.ufmg.br/index.php/jidm/article/view/276

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