BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Distributed deviation detection in sensor networks
ACM SIGMOD Record
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
Example-Based Robust Outlier Detection in High Dimensional Datasets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
Knowledge and Information Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Hos-Miner: a system for detecting outlyting subspaces of high-dimensional data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.