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
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A Similarity Search Method of Time Series Data with Combination of Fourier and Wavelet Transforms
TIME '02 Proceedings of the Ninth International Symposium on Temporal Representation and Reasoning (TIME'02)
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
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
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Multiscale Comparison of Three-Dimensional Trajectories: A Preliminary Step
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
On constructing clusters from non-euclidean dissimilarity matrix by using rough clustering
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
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This paper presents a novel method called modified multiscale matching, that enable us to multiscale structural comparison of irregularly-sampled, different-length time series like medical data. We revised the conventional multiscale matching algorithm so that it produces sequence dissimilarity that can be further used for clustering. The main improvements are: (1) introduction of a new segment representation that elude the problem of shrinkage at high scales, (2) introduction of a new dissimilarity measure that directly reflects the dissimilarity of sequence values. We examined the usefulness of the method on the cylinder-bell-funnel dataset and chronic hepatitis dataset. The results demonstrated that the dissimilarity matrix produced by the proposed method, combined with conventional clustering techniques, lead to the successful clustering for both synthetic and real-world data.