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The DDoutlier package provides users with a wide variety of distance- and density-based outlier detection functions. Distance- and density based outlier detection works with local outliers in a multidimensional domain, meaning observations are compared to their respective neighborhood. The algorithms mainly have an advantage within two domains:
All functions require a dataset as input and have a varying number of
input parameters influencing the outlier score output. The most common
input parameter is the k parameter for constructing the k-nearest
neighborhood. To speed up kNN search, the kNN function in
the dbscan package is used to construct a kd-tree. For the
functions COF, LOCI and LDOF a
complete distance matrix is required, leaving out the possibility of
using a kd-tree. For the functions RDOS, INFLO
and NOF computation of a reverse neighborhood is required,
also making it computational heavy.
Removing duplicates and standardizing data is recommended before computing outlier scores.
To install latest version in R use following commands:
#install.packages('devtools')
#devtools::install_github('jhmadsen/DDoutlier')Work is currently carried out to make it available in the CRAN repository
Angiulli, F., & Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces. In Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD). Helsinki, Finland. pp. 15-26. DOI: 10.1007/3-540-45681-3_2
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. In Int. Conf. On Management of Data. Dallas, TX. pp. 93-104. DOI: 10.1145/342009.335388
Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011). RKOF: Robust Kernel-Based Local Outlier Detection. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining. pp. 270-283. DOI: 10.1007/978-3-642-20847-8_23
Hautamaki, V., & Ismo, K. (2004). Outlier Detection Using k-Nearest Neighbour Graph. In International Conference on Pattern Recognition. Cambridge, UK. pp. 430-433. DOI: 10.1109/ICPR.2004.1334558
Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015). A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems. pp. 71-77. DOI: 10.1016/j.knosys.2015.10.014
Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006). Ranking Outliers Using Symmetric Neighborhood Relationship. In Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Singapore. pp 577-593. DOI: 10.1007/11731139_68
Knorr, M., & Ng, R. T. (1997). A Unified Approach for Mining Outliers. In Conf. of the Centre for Advanced Studies on Collaborative Research (CASCON). Toronto, Canada. pp. 236-248. DOI: 10.1145/782010.782021
Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009). LoOP: Local Outlier Probabilities. In ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China. pp. 1649-1652. DOI: 10.1145/1645953.1646195
Latecki, L., Lazarevic, A. & Prokrajac, D. (2007). Outlier Detection with Kernel Density Functions. International Workshop on Machine Learning and Data Mining in Pattern Recognition: Machine Learning and Data Mining in Pattern Recognition. pp. 61-75. DOI: 10.1007/978-3-540-73499-4_6
Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003). LOCI: Fast Outlier Detection Using the Local Correlation Integral. In International Conference on Data Engineering. pp. 315-326. DOI: 10.1109/ICDE.2003.1260802
Schubert, E., Zimek, A. & Kriegel, H-P. (2014). Generalized Outlier Detection with Flexible Kernel Density Estimates. Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, USA. pp. 542-550. DOI: 10.1137/1.9781611973440.63
Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002). Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Taipei. pp. 535-548. DOI: 10.1007/3-540-47887-6_53
Zhang, K., Hutter, M. & Jin, H. (2009). A New Local Distance-based Outlier Detection Approach for Scattered Real-World Data. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining. pp. 813-822. DOI: 10.1007/978-3-642-01307-2_84
Zhu, Q., Feng, Ji. & Huang, J. (2016). Natural neighbor: A self-adaptive neighborhood method without parameter K. Pattern Recognition Letters. pp. 30-36. DOI: 10.1016/j.patrec.2016.05.007
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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