Clustering of multivariate time-series data

A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is based on the Mahalanobis distance between the datasets. The standard K-means algorithm is modified to cluster multivariate time-series datasets using similarity factors. Data from a highly nonlinear acetone-butanol fermentation example are clustered to demonstrate the effectiveness of the proposed methodology. Comparisons with existing clustering methods show several advantages of the proposed methodology.