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Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Sequence Learning - Paradigms, Algorithms, and Applications
Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Clustering Time Series with Clipped Data
Machine Learning
Multimedia retrieval using time series representation and relevance feedback
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Integral shape averaging and structural average estimation: a comparative study
IEEE Transactions on Signal Processing - Part I
Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment
Theoretical Computer Science
Shape-based template matching for time series data
Knowledge-Based Systems
Shape-Based clustering for time series data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Modeling topic trends on the social web using temporal signatures
Proceedings of the twelfth international workshop on Web information and data management
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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Shape averaging or signal averaging of time series data is one of the prevalent subroutines in data mining tasks, where Dynamic Time Warping distance measure (DTW) is known to work exceptionally well with these time series data, and has long been demonstrated in various data mining tasks involving shape similarity among various domains. Therefore, DTW has been used to find the averageshape of two time series according to the optimal mapping between them. Several methods have been proposed, some of which require the number of time series being averaged to be a power of two. In this work, we will demonstrate that these proposed methods cannot produce the realaverage of the time series. We conclude with a suggestion of a method to potentially find the shape-based time series average.