Abstract
In this paper we present an algorithm for outlier detection in high-dimensional spaces based on constrained particle swarm optimization techniques. The concept of outliers is defined as sparsely populated patterns in lower dimensional subspaces. The search for best abnormally sparse subspaces is done by an innovative use of particle swarm optimization methods with a specifically designed particle coding and conversion strategy as well as some dimensionality-preserving updating techniques. Experimental results show that the proposed algorithm is feasible and effective for high-dimensional outlier detection problems.
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References
Knorr, E.M., Ng, R.T.: Algorithms for Mining Distance-Based Outliers in Large Datasets. In: 24th International Conference on Very Large Databases, pp. 392–403. ACM Press, New York (1998)
Knorr, E.M., Ng, R.T.: Finding Intensional Knowledge of Distance-based Outliers. In: 25th International Conference on Very Large Data Bases, pp. 211–222. Morgan Kaufmann, Edinburgh (1999)
Ramaswamy, S., Rastogi, R., Kyuseok, S.: Efficient Algorithms for Mining Outliers From LargeData Sets. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 427–438. ACM Press, Dallas (2000)
Knorr, E.M., Ng, R.T., Tukacov, V.: Distance-based Outliers: Algorithms and Applications. The VLDB Journal 8, 237–253 (2000)
Angiulli, F., Basta, S., Pizzuti, C.: Distance-Based Detection and Prediction of Outliers. IEEE Transactions on Knowlodge and Data Engineering 18, 145–160 (2006)
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is Nearest Neighbors Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)
Aggarwal, C., Yu, P.: An Effective and Efficient Algorithm for High-Dimensional Outlier Detection. The VLDB Journal 14, 211–221 (2005)
Chen, G.P., Ye, D.Y.: An Improved Evolutinary Algorithm Based Approach for Outlier Detection in High Dimensional Spaces. Journal of Communication and Computer 3, 5–8 (2006)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Clerc, M.: Discrete Particle Swarm Optimization: New Optimization Techniques in Engineering. Springer, Heidelberg (2004)
Zeng, J.C.: Particle Swarm Optimization Algorithms. Science Press, Beijing (2004)
Ye, D.Y., Chen, Z.J., Liao, J.K.: A New Algorithm for Minimum Attribute Reduction Based on Binary Particle Swarm Optimization with Vaccination. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 1029–1036. Springer, Heidelberg (2007)
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Ye, D., Chen, Z. (2008). A New Algorithm for High-Dimensional Outlier Detection Based on Constrained Particle Swarm Intelligence. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_70
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DOI: https://doi.org/10.1007/978-3-540-79721-0_70
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