Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
Distance-Based Detection and Prediction of Outliers
IEEE Transactions on Knowledge and Data Engineering
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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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.