Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape representation and recognition from multiscale curvature
Computer Vision and Image Understanding
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Structure-Based Statistical Features and Multivariate Time Series Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Cluster Analysis
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Multidimensional temporal mining in clinical data
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Capturing behavior of medical staff: a similarity-oriented temporal data mining approach
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Similarity-based behavior and process mining of medical practices
Future Generation Computer Systems
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This paper proposes a method for grouping trajectories as two-dimensional time-series data. Our method employed a two-stage approach. Firstly, it compared two trajectories based on their structural similarity, and determines the best correspondence of partial trajectories. Then, it calculated the value-based dissimilarity for the all pairs of matched segments, and outputs their total sum as the dissimilarity of two trajectories. We evaluated this method on two data sets. Experimental results on the Australia sign language dataset and chronic hepatitis dataset demonstrate that our method could capture the structural similarity between trajectories even in the presence of noise and local differences, and could provide better proximity for discriminating objects.