Most of the approaches coping with concept drift described in the machine learning literature are focused solely on detecting the concept changes or adapting the classification system, and hardly any works exist, which try to also describe the changes in concept. Nowadays, we desire methods, which are able to detect the concept drift in the absence of information about class labels. In this article, we present a semi-supervised method and analyze the possibilities of using the simulated concept recurrence against concept drift and also expanding the previously presented functionality of the algorithm from the sole concept characterization to both concept drift detection and concept characterization. The supervision is limited to the system setup phase, and during the evaluation of the algorithm, we assume no support from the experts. Further comparing this work to our previous publication, the scope of experiments has been extended from the single concept drift problems to the multi-concept scenarios, and also, the new method is evaluated on three different levels of the prior knowledge presented to the system beforehand.