Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICDM.2007.103 | ICDM |
Keywords | Field | DocType |
human motion sequence,pattern clustering,univariate time series,structure-based statistical features,univariate time series structure,statistical analysis,multivariate time series clustering,statistical feature,structure-based statistical feature extraction,k-means clustering,dimension-reduction procedure,cluster multivariate time series,multivariate time series,fixed-length vector,feature extraction,high-dimensional multivariate time series,time series,hierarchical clustering,new method,pattern recognition,dimension reduction,k means,k means clustering | Data mining,Fuzzy clustering,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Hierarchical clustering,k-medians clustering,Canopy clustering algorithm,Correlation clustering,Pattern recognition,Univariate,Machine learning | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-0-7695-3018-5 | 29 |
PageRank | References | Authors |
1.69 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaozhe Wang | 1 | 255 | 22.84 |
| Anthony Wirth | 2 | 593 | 40.40 |
| Liang Wang | 3 | 4317 | 243.28 |