DDoutlier

Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) doi:10.1145/782010.782021, Angiulli, F., & Pizzuti, C. (2002) doi:10.1007/3-540-45681-3_2, Hautamaki, V., & Ismo, K. (2004) doi:10.1109/ICPR.2004.1334558 and Zhang, K., Hutter, M. & Jin, H. (2009) doi:10.1007/978-3-642-01307-2_84. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) doi:10.1007/3-540-47887-6_53, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) doi:10.1007/11731139_68, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) doi:10.1137/1.9781611973440.63, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) doi:10.1007/978-3-540-73499-4_6, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) doi:10.1109/ICDE.2003.1260802, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) doi:10.1145/342009.335388, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) doi:10.1145/1645953.1646195, Zhu, Q., Feng, Ji. & Huang, J. (2016) doi:10.1016/j.patrec.2016.05.007, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) doi:10.1016/j.knosys.2015.10.014, Tang, B. & Haibo, He. (2017) doi:10.1016/j.neucom.2017.02.039 and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) doi:10.1007/978-3-642-20847-8_23.