Abstract
Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier. Instead, many approaches give an “outlier score” or “outlier factor” indicating “how much” the respective data object is an outlier. Such outlier scores differ widely in their range, contrast, and expressiveness between different outlier models. Even for one and the same outlier model, the same score can indicate a different degree of “outlierness” in different data sets or regions of different characteristics in one data set. Here, we demonstrate a visualization tool based on a unification of outlier scores that allows to compare and evaluate outlier scores visually even for high dimensional data.
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References
Achtert, E., Bernecker, T., Kriegel, H.P., Schubert, E., Zimek, A.: ELKI in time: ELKI 0.2 for the performance evaluation of distance measures for time series. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 436–440. Springer, Heidelberg (2009)
Achtert, E., Kriegel, H.P., Zimek, A.: ELKI: a software system for evaluation of subspace clustering algorithms. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 580–585. Springer, Heidelberg (2008)
Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, p. 15. Springer, Heidelberg (2002)
Barnett, V., Lewis, T.: Outliers in Statistical Data, 3rd edn. John Wiley & Sons, Chichester (1994)
Breunig, M.M., Kriegel, H.P., Ng, R., Sander, J.: LOF: Identifying density-based local outliers. In: Proc. SIGMOD (2000)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proc. VLDB (1998)
Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: LoOP: local outlier probabilities. In: Proc. CIKM (2009)
Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Proc. PAKDD (2009)
Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proc. KDD (2008)
Papadimitriou, S., Kitagawa, H., Gibbons, P., Faloutsos, C.: LOCI: Fast outlier detection using the local correlation integral. In: Proc. ICDE (2003)
Pei, Y., Zaïane, O., Gao, Y.: An efficient reference-based approach to outlier detection in large datasets. In: Proc. ICDM (2006)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proc. SIGMOD (2000)
Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. In: Proc. PAKDD (2009)
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Achtert, E., Kriegel, HP., Reichert, L., Schubert, E., Wojdanowski, R., Zimek, A. (2010). Visual Evaluation of Outlier Detection Models. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12098-5_34
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DOI: https://doi.org/10.1007/978-3-642-12098-5_34
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