The notion of concept drift refers to the phenomenon that the data distribution changes over time. If drift occurs, machine learning models need adjustment. Since drift can be inhomogeneous, suitable actions depending on the location in data space. In this paper we address the challenge to partition the data space into segments with homogeneous drift characteristics. We formalize this objective as an independence criterion, and derive a robust and efficient training algorithm based thereon. We

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... valuate the efficiency of the method in comparison to existing technologies: the identification of drifting clusters, and the estimation of a conditional density distribution. * Funding in the frame of the BMBF project ITS.ML, 01IS18041A is gratefully acknowledged.