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
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th 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
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Neural Networks for Novelty Detection in Airframe Strain Data
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Search Over Time-Series Data Using Wavelets
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
SAXually Explicit Images: Finding Unusual Shapes
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Given a set of training instances of sequences (time series), the problem of anomalistic sequence detection is to predict whether a newly observed time series novel or normal. Anomalistic sequence detection is very useful in many monitoring applications such as video surveillance and signal recognition. In this paper, we extend existing distance-based outlier detection algorithms to address the anomaly detection problem, and propose an instance-based anomaly detection algorithm. We study the effectiveness of these algorithms on some commonly used distance measures of time series. Experiments show that the instance-based algorithm under warping distances such as DTW and EDR achieves much better accuracy than the other combination. We observe that the actual distance calculation of warping distances contributes the main bulk of computational cost of anomaly detection. To improve the efficiency, we propose a local instance summarisation approach, called VarSpace, which reduces distance calculation by summarising similar training instances. Experiments show that the VarSpace approach can improve the efficiency of instance-based anomaly detection significantly.