  Pattern discovery in multidimensional data sets has been the subject of
research for decades. There exists a wide spectrum of clustering algorithms
that can be used for this purpose. However, their practical applications share
a common post-clustering phase, which concerns expert-based interpretation and
analysis of the obtained results. We argue that this can be the bottleneck in
the process, especially in cases where domain knowledge exists prior to
clustering. Such a situation requires not only a proper analysis of
automatically discovered clusters but also conformance checking with existing
knowledge. In this work, we present Knowledge Augmented Clustering (KnAC). Its
main goal is to confront expert-based labelling with automated clustering for
the sake of updating and refining the former. Our solution is not restricted to
any existing clustering algorithm. Instead, KnAC can serve as an augmentation
of an arbitrary clustering algorithm, making the approach robust and a
model-agnostic improvement of any state-of-the-art clustering method. We
demonstrate the feasibility of our method on artificially, reproducible
examples and in a real life use case scenario. In both cases, we achieved
better results than classic clustering algorithms without augmentation.
