Fundamentals of speech recognition
Fundamentals of speech recognition
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A generic motif discovery algorithm for sequential data
Bioinformatics
Fast nonparametric machine learning algorithms for high-dimensional massive data and applications
Fast nonparametric machine learning algorithms for high-dimensional massive data and applications
Improving activity discovery with automatic neighborhood estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Discovering frequent work procedures from resource connections
Proceedings of the 14th international conference on Intelligent user interfaces
On-line motif detection in time series with SwiftMotif
Pattern Recognition
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A tree-construction search approach for multivariate time series motifs discovery
Pattern Recognition Letters
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Parallel exact time series motif discovery
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Lag patterns in time series databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
A review on time series data mining
Engineering Applications of Artificial Intelligence
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Discovering deformable motifs in continuous time series data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Unsupervised temporal commonality discovery
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
The Journal of Machine Learning Research
A personalized exercise trainer for the elderly
Journal of Ambient Intelligence and Smart Environments
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The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and non-linear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.