Dataset clustering could have more than one “right” result depending on a user intention. For example, texts could be clustered according to their topic, style or author. In case of unsatisfactory results, a data scientist needs to re-construct a feature space in order to change the results. The relation between the feature space and the result are often quite complicated. The latter results in building several clustering models to explore useful relations. Interactive clustering with feedback is aimed to cope with this problem. In this paper an approach to user feedback processing during clustering is presented. The approach is based on end-to-end clustering and uses an autoencoder neural network. This technique allows to adjust iteratively the computing clusters without changing feature space.