Multivariate time-series clustering based on component relationship networks

Abstract

Clustering is a powerful technique for providing class labels of data objects for learning guidance. Because traditional clustering methods rarely consider the component correlations of a multivariate time series(MTS), an MTS clustering method based on a component relationship network (CRN) is proposed in the present study. An MTS dataset is mapped to a multi-relationship network (MRN), which consists of a set of CRNs. Every CRN reflects the relationship of the MTS data under each component. The proposed method applies two steps. First, the distance function and K-nearest neighbors are combined to transform an MTS dataset into an MRN. Second, a non-negative matrix factorization is designed to identify time-series clusters. Because asynchronous time-series data exist in the form of an approximate case, we recommend an improved penalty-coefficient based dynamic time-warping algorithm to measure the similarity between two-time sequences. The similarity can reflect the correlation between the asynchronous MTS data. Through an experiment, we compared the proposed method to other clustering methods and discussed of the effects of the parameters in detail. The numerical results of the experiment demonstrate that the proposed method can improve the accuracy and quality of MTS data clustering.