Effective and robust mining of temporal subspace clusters

Mining temporal multivariate data by clustering is an important research topic. In today's complex data, interesting patterns are often neither bound to the whole dimensional nor temporal extent of the data domain. This challenge is met by temporal subspace clustering methods. Their effectiveness, however, is impeded by aspects unavoidable in real world data: Misalignments between time series, for example caused by out-of-sync sensors, and measurement errors. Under these conditions, existing temporal subspace clustering approaches miss the patterns contained in the data. In this paper, we propose a novel clustering method that mines temporal subspace clusters reflected by sets of objects and relevant intervals. We enable flexible handling of misaligned time series by adaptively shifting time series in the time domain, and we achieve robustness to measurement errors by allowing certain fractions of deviating values in each relevant point in time. We show the effectiveness of our method in experiments on real and synthetic data.