Combination of generative models and SVM based classifier for speech emotion recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A review on time series data mining
Engineering Applications of Artificial Intelligence
Detection of risk factors using trajectory mining
Journal of Intelligent Information Systems
Wavelets-based clustering of multivariate time series
Fuzzy Sets and Systems
Mining of temporal coherent subspace clusters in multivariate time series databases
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Unsupervised categorization of human motion sequences
Intelligent Data Analysis
| Hi-index | 0.00 |
We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.