Interaction-based Clustering of Multivariate Time Series

In this paper, we present a novel approach to clustering multivariate time series. In contrast to previous approaches, we base our cluster notion on the interactions between the univariate time series within a data object. Our objective is to assign objects with a similar intrinsic interaction pattern to a common cluster. To formalize this idea, we define a cluster by a set of mathematical models describing the cluster-specific interaction pattern. In addition, we propose interaction K-means (IKM), an efficient algorithm for partitioning clustering of multivariate time series. The cluster-specific interaction patterns detected by IKM provide valuable information for interpretation of the cluster content. An extensive experimental evaluation on synthetic and real world data demonstrates the effectiveness and efficiency of our approach.