The goal of this thesis is the identiﬁcation of relationships between data streams. These relationships may change over time. The research contribution is to solve this problem by combining two data mining ﬁelds. The ﬁrst ﬁeld is the identiﬁcation of such relationships, e.g. by using correlation measures. The second ﬁeld covers methods to deal with the dynamics of such a system which require model reconsideration. This ﬁeld is called “concept drift” and allows to identify and handle new situations. In this thesis two different approaches are presented to combine these two ﬁelds into one solution. After that, these two approaches are assessed on synthetic and real-world datasets. Finally, the solution is applied to the ﬁnance domain. The task is the determination of dominant factors inﬂuencing exchange rates. Finance experts call such a dominant factor “regime”. These factors change over time and thus, the problem is named “regime drift”. The approach turns out to be successful in dealing with regime drifts.