Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Disk aware discord discovery: finding unusual time series in terabyte sized datasets
Knowledge and Information Systems
An Alternative Approach to Computing Shape Orientation with an Application to Compound Shapes
International Journal of Computer Vision
Finding anomalous periodic time series
Machine Learning
Artificial Intelligence in Medicine
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
A review on time series data mining
Engineering Applications of Artificial Intelligence
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Anomalistic sequence detection
International Journal of Intelligent Information and Database Systems
Rotation-invariant similarity in time series using bag-of-patterns representation
Journal of Intelligent Information Systems
Genetic algorithms-based symbolic aggregate approximation
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Time series representation: a random shifting perspective
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Finding time series discord based on bit representation clustering
Knowledge-Based Systems
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Over the past three decades, there has been a great deal of research on shape analysis, focusing mostly on shape indexing, clustering, and classification. In this work, we introduce the new problem of finding shape discords, the most unusual shapes in a collection. We motivate the problem by considering the utility of shape discords in diverse domains including zoology, anthropology, and medicine. While the brute force search algorithm has quadratic time complexity, we avoid this by using locality-sensitive hashing to estimate similarity between shapes which enables us to reorder the search more efficiently. An extensive experimental evaluation demonstrates that our approach can speed up computation by three to four orders of magnitude.