Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Video summarization by curve simplification
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Motion Recognition Method by Using Primitive Motions
VDB 5 Proceedings of the Fifth Working Conference on Visual Database Systems: Advances in Visual Information Management
Extraction of Primitive Motion for Human Motion Recognition
DS '99 Proceedings of the Second International Conference on Discovery Science
Discovering of Correlation from Multi-stream of Human Motion
DS '00 Proceedings of the Third International Conference on Discovery Science
IEEE Transactions on Computers
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
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
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Personalized behavior pattern recognition and unusual event detection for mobile users
Mobile Information Systems
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In past several years, more and more digital multimedia data in the forms of image, video and audio have been captured and archived. This kind of new resource is exiting, yet the sheer volume of data makes any retrieval task overwhelming and its efficient usage impossible. In order to deal with the deficiency, tagging method is required so as to browse the content of multimedia data almost instantly.In this paper, we will focus on tagging human motion data. The motion data have the following features: movements of some body parts have influence on other body parts. We call this dependency motion association rule. Thus, the task of tagging motion data is equal to the task of expressing motion by using motion association rules. Association rules consist of symbols, which uniquely represent basic patterns. We call these basic patterns primitive motions. Primitive motions are extracted from the motion data by using segmentation and clustering processes. Finally, we will discuss some experiments to discover association rules from multi-stream of the motion data.