Abstract
We are interested in the problem of outlier detection, which is the discovery of data that deviate a lot from other data patterns. Hawkins [7] characterizes an outlier in a quite intuitive way as follows: An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism.
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References
Ankerst, M., Breunig, M., Kriegel, H.P., Sander, J.: OPTICS: Ordering points to identify the cluster structure. In: Proc. of ACM-SIGMOD Conf., pp. 49–60 (1999)
Arning, A., Agrawal, R., Raghavan, P.: A Linear Method for Deviation detection in Large Databases. In: Proc. of 2nd Intl. Conf. On Knowledge Discovery and Data Mining, pp. 164–169 (1996)
Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley, Chichester (1994)
Breuning, M., Kriegel, H.-P., Ng, R., Sander, J.: LOF: Identifying density-based Local Outliers. In: Proc. of the ACM SIGMOD Conf. (2000)
DuMouchel, W., Schonlau, M.: A Fast Computer Intrusion Detection Algorithm based on Hypothesis Testing of Command Transition Probabilities. In: Proc. of 4th Intl. Conf. On Knowledge Discovery and Data Mining, pp. 189–193 (1998)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. of 2nd Intl. Conf. On Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Fawcett, T., Provost, F.: Adaptive Fraud Detection. Data Mining and Knowledge Discovery Journal 1(3), 291–316 (1997)
Guha, S., Rastogi, R., Shim, K.: Cure: An Efficient Clustering Algorithm for Large Databases. In: Proc. of the ACM SIGMOD Conf., pp. 73–84 (1998)
Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)
Knorr, E., Ng, R.: Algorithms for Mining Distance-based Outliers in Large Datasets. In: Proc. of 24th Intl. Conf. On VLDB, pp. 392–403 (1998)
Knorr, E., Ng, R.: Finding Intensional Knowledge of Distance-based Outliers. In: Proc. of 25th Intl. Conf. On VLDB, pp. 211–222 (1999)
Ng, R., Han, J.: Efficient and Effective Clustering Methods for Spatial Data Mining. In: Proc. of 20th Intl. Conf. On Very Large Data Bases, pp. 144–155 (1994)
Ramaswamy, S., Rastogi, R., Kyuseok, S.: Efficient Algorithms for Mining Outliers from Large Data Sets. In: Proc. of ACM SIGMOD Conf., pp. 427–438 (2000)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest Neighbor Queries. In: Proc. of ACM SIGMOD Conf., pp. 71–79 (1995)
Sheikholeslami, G., Chatterjee, S., Zhang, A.: WaveCluster: A multi-Resolution Clustering Approach for Very Large Spatial Databases. In: Proc. of 24th Intl. Conf. On Very Large Data Bases, pp. 428–439 (1998)
Tang, J., Chen, Z., Fu, A., Cheung, D.: A Robust Outlier Detection Scheme in Large Data Sets. In: PAKDD (2002)
Zhang, T., Ramakrishnan, R., Linvy, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proc. of ACM SIGMOD Intl. Conf., pp. 103–114 (1996)
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Chen, Z., Fu, A.WC., Tang, J. (2003). On Complementarity of Cluster and Outlier Detection Schemes. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_24
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DOI: https://doi.org/10.1007/978-3-540-45228-7_24
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