Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
One-Pass Wavelet Decompositions of Data Streams
IEEE Transactions on Knowledge and Data Engineering
One-class svms for document classification
The Journal of Machine Learning Research
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier Detection Using k-Nearest Neighbour Graph
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Approximations to Magic: Finding Unusual Medical Time Series
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
ACM SIGMOD Record
Modeling Multiple Time Series for Anomaly Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Finding time series discords based on haar transform
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
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We consider the problem of anomaly detection in data streams, which is the problem of extracting subsequences that do not match an expected behaviour. The main challenge for detecting anomalous subsequences from data streams in the existing techniques is to determine the lengths of the normal and anomalous subsequences. Therefore, creating a robust model for detecting the anomalous subsequences is of critical importance. In this paper, we propose an incremental algorithm based on the dynamic time warping technique to detect anomalous subsequences in data streams. The proposed algorithm is able to detect anomalous subsequences under relaxed length constrains of the normal and/or the anomalous subsequences. That is the proposed algorithm is able to detect variable length anomalous subsequences from among variable length normal sequences. The proposed robust model can be applied in areas such as system health monitoring, event detection in sensor networks, and detecting eco-system disturbances, etc. The cost of the proposed algorithm is linear with time and memory.