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
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient range-constrained similarity search on wavelet synopses over multiple streams
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Detecting Abnormal Trend Evolution over Multiple Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
ACM Computing Surveys (CSUR)
Efficient discovery of unusual patterns in time series
New Generation Computing
Designing an expert system for fraud detection in private telecommunications networks
Expert Systems with Applications: An International Journal
Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
A probabilistic approach to fraud detection in telecommunications
Knowledge-Based Systems
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Abnormal pattern detection is an important task in series data anomaly detection. Because of the noise interference, the accuracy of abnormal detection method based on deterministic value is decreased. Whereas, most recent studies aimed at solving the anomaly detection problem in uncertain series use possible world models to describe the uncertainty in discrete data and select outliers as the anomaly detection objects. Abnormal pattern detection problems in continuous uncertain data are rarely reported. In order to improve the accuracy of abnormal pattern detection for uncertain data, we propose a Probabilistic Distance based approach for mining Abnormal Pattern Detection from uncertain series data (PD_ APD). Our considered approach re-express the Euclidean distance according to data's probability density function (PDF), and get a probabilistic metric to compute the dissimilarity of two uncertain series. Our experiments show that, compared with Tarzan, a deterministic approach that directly processes data without considering uncertainty, PD_ APD provides a flexible trade-off between false alarms and miss ratios by controlling a probabilistic abnormality threshold. Especially, when data uncertain variance is large, PD_ APD has lower false alarms under the same specific miss ratio.