Concept decompositions for large sparse text data using clustering
Machine Learning
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th 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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Automatic thresholding for defect detection
Pattern Recognition Letters
Finding anomalous periodic time series
Machine Learning
Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications
IEEE Transactions on Information Technology in Biomedicine
Finding time series discord based on bit representation clustering
Knowledge-Based Systems
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Objective: Detecting discords in time series is a special novelty detection task that has found many interesting applications. Unlike the traditional novelty detection methods which can make use of a separate set of normal samples to build up the model, discord detection is often provided with mixed data containing both normal and abnormal data. The objective of this work is to present an effective method to detect discords in unsynchronized periodic time series data. Methods: The task of discord detection is considered as a problem of unsupervised learning with noise data. A new clustering algorithm named weighted spherical 1-mean with phase shift (PS-WS1M) is proposed in this work. It introduces a phase adjustment procedure into the iterative clustering process and produces a set of anomaly scores based upon which an unsupervised approach is employed to locate the discords automatically. A theoretical analysis on the robustness and convergence of PS-WS1M is also given. Results: The proposed algorithm is evaluated via real-world electrocardiograms datasets extracted from the MIT-BIH database. The experimental results show that the proposed algorithm is effective and competitive for the problem of discord detection in periodic time series. Meanwhile, the robustness of PS-WS1M is also experimentally verified. As compared to some of the other discord detection methods, the proposed algorithm can always achieve ideal FScore values with most of which exceeding 0.98. Conclusion: The proposed PS-WS1M algorithm allows the integration of a phase adjustment procedure into the iterative clustering process and it can be successfully applied to detect discords in time series.