  Industrial transfer learning increases the adaptability of deep learning
algorithms towards heterogenous and dynamic industrial use cases without high
manual efforts. The appropriate selection of what to transfer can vastly
improve a transfer's results. In this paper, a transfer case selection based
upon clustering is presented. Founded on a survey of clustering algorithms, the
BIRCH algorithm is selected for this purpose. It is evaluated on an industrial
time series dataset from a discrete manufacturing scenario. Results underline
the approaches' applicability caused by its results' reproducibility and
practical indifference to sequence, size and dimensionality of (sub-)datasets
to be clustered sequentially.
