Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Crisp and fuzzy k-means clustering algorithms for multivariate functional data
Computational Statistics
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
Clustering of biological time series by cepstral coefficients based distances
Pattern Recognition
Structure-Based Statistical Features and Multivariate Time Series Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Autocorrelation-based fuzzy clustering of time series
Fuzzy Sets and Systems
Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series
Fuzzy Sets and Systems
Clustering of time series data-a survey
Pattern Recognition
A Fuzzy Clustering Model for Multivariate Spatial Time Series
Journal of Classification
Wavelet-based Fuzzy Clustering of Time Series
Journal of Classification
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Independent component analysis for clustering multivariate time series data
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals
Computational Statistics & Data Analysis
| Hi-index | 0.20 |
Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet features are considered for the clustering of multivariate time series. The performance of each of these methods is evaluated for stationary and variance nonstationary multivariate time series with different error correlation structures. The main outcomes of the simulation studies are are as follows: the superior performance of this approach for both the crisp and fuzzy cluster methods compared to some of the other approaches for clustering multivariate time series; the very good performance of the fuzzy relational method, overall, to cluster longer time series when all of them do not appear to group exclusively into well separated clusters. We consider an application to multivariate greenhouse gases time series and show that the crisp and fuzzy clustering methods considered are well validated.