Abstract
Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.
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References
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: VLDB 2003: Proceedings of the 29th international conference on Very large data bases, pp. 81–92. VLDB Endowment (2003)
Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: CIKM 2007: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 811–820. ACM, New York (2007)
Barnett, V., Lewis, T.: Outliers in statistical data (1984)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)
Cao, H., Zhou, Y., Shou, L., Chen, G.: Attribute outlier detection over data streams, 9 (2009), http://db.zju.edu.cn/wiki/index.php/Hui_Cao
Hawkins, D.: Identification of outliers. Chapman and Hall, Reading (1980)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)
Jiang, M.-F., Tseng, S.-S., Su, C.-M.: Two-phase clustering process for outliers detection. Pattern Recognition Letters 22(6/7), 691–700 (2001)
Knorr, E.M., Ng, R.T.: A unified notion of outliers: Properties and computation. In: KDD, pp. 219–222 (1997)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB 1998: Proceedings of the 24th International Conference on Very Large Data Bases, pp. 392–403. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Koh, J.L.Y., Lee, M.-L., Hsu, W., Ang, W.T.: Correlation-based attribute outlier detection in XML. In: ICDE 2008: Proceedings of the 24th International Conference on Data Engineering, pp. 1522–1524 (2008)
Koh, J.L.Y., Lee, M.-L., Hsu, W., Lam, K.-T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 164–175. Springer, Heidelberg (2007)
Zhang, J., Gao, Q., Wang, H.: Spot: A system for detecting projected outliers from high-dimensional data streams. In: ICDE 2008: Proceedings of the 24th International Conference on Data Engineering, pp. 1628–1631. IEEE, Los Alamitos (2008)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: SIGMOD 1996: Proceedings of the 1996 ACM SIGMOD international conference on Management of data, pp. 103–114. ACM, New York (1996)
Zhou, A., Cao, F., Qian, W., Jin, C.: Tracking clusters in evolving data streams over sliding windows. Knowl. Inf. Syst. 15(2), 181–214 (2008)
Zhu, X., Wu, X.: Class noise vs. attribute noise: A quantitative study. Artif. Intell. Rev. 22(3), 177–210 (2004)
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Cao, H., Zhou, Y., Shou, L., Chen, G. (2010). Attribute Outlier Detection over Data Streams. 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_17
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DOI: https://doi.org/10.1007/978-3-642-12098-5_17
Publisher Name: Springer, Berlin, Heidelberg
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