  The last decades have not only been characterized by an explosive growth of
data, but also an increasing appreciation of data as a valuable resource. Their
value comes with the ability to extract meaningful patterns that are of
economic, societal or scientific relevance. A particular challenge is the
identification of patterns across time, including those that might only become
apparent when the temporal dimension is taken into account. Here, we present a
novel method that aims to achieve this by detecting dynamic clusters, i.e.
structural elements that can be present over prolonged durations. It is based
on an adaptive identification of majority overlaps between groups at different
time points and accommodates the transient decompositions in otherwise
persistent dynamic clusters. Our method enables the detection of persistent
structural elements with internal dynamics and can be applied to any
classifiable data, ranging from social contact networks to arbitrary sets of
time stamped feature vectors. It represents a unique tool to study systems with
non-trivial temporal dynamics and has a broad applicability to scientific,
societal and economic data.
